Pedestrian detection in low-light conditions: A comprehensive survey

Autonomous vehicles; Computer vision; Object detection; Pedestrian detection; Autonomous driving; Objects detection; Signal Processing; Computer Vision and Pattern Recognition; Computer Science - Computer Vision and Pattern Recognition

Résumé :

[en] Pedestrian detection remains a critical problem in various domains, such as computer vision, surveillance, and autonomous driving. In particular, accurate and instant detection of pedestrians in low-light conditions and reduced visibility is of utmost importance for autonomous vehicles to prevent accidents and save lives. This paper aims to comprehensively survey various pedestrian detection approaches, baselines, and datasets that specifically target low-light conditions. The survey discusses the challenges faced in detecting pedestrians at night and explores state-of-the-art methodologies proposed in recent years to address this issue. These methodologies encompass a diverse range, including deep learning-based, feature-based, and hybrid approaches, which have shown promising results in enhancing pedestrian detection performance under challenging lighting conditions. Furthermore, the paper highlights current research directions in the field and identifies potential solutions that merit further investigation by researchers. By thoroughly examining pedestrian detection techniques in low-light conditions, this survey seeks to contribute to the advancement of safer and more reliable autonomous driving systems and other applications related to pedestrian safety. Accordingly, most of the current approaches in the field use deep learning-based image fusion methodologies (i.e., early, halfway, and late fusion) for accurate and reliable pedestrian detection. Moreover, the majority of the works in the field (approximately 48%) have been evaluated on the KAIST dataset, while the real-world video feeds recorded by authors have been used in less than 6 % of the works.

Centre de recherche :

Interdisciplinary Centre for Security, Reliability and Trust (SnT) > ARG - Automation & Robotics

Disciplines :

Sciences informatiques

Auteur, co-auteur :

Ghari, Bahareh; Department of Computer Engineering, University of Guilan, Rasht, Iran

TOURANI, Ali ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Automation

Shahbahrami, Asadollah; Department of Computer Engineering, University of Guilan, Rasht, Iran

Gaydadjiev, Georgi; Department of Quantum and Computer Engineering, Delft University of Technology, Delft, Netherlands

Co-auteurs externes :

yes

Langue du document :

Anglais

Titre :

Pedestrian detection in low-light conditions: A comprehensive survey

Date de publication/diffusion :

août 2024

Titre du périodique :

Image and Vision Computing

ISSN :

0262-8856

Maison d'édition :

Elsevier Ltd

Volume/Tome :

148

Pagination :

105106

Peer reviewed :

Peer reviewed vérifié par ORBi

Focus Area :

Computational Sciences

Objectif de développement durable (ODD) :

9. Industrie, innovation et infrastructure

URL complémentaire :

https://www.sciencedirect.com/science/article/pii/S0262885624002105?via%3Dihub

Commentaire :

23 pages, 3 tables, 10 figures

Disponible sur ORBilu :

depuis le 17 juillet 2024

Statistiques

Nombre de vues

268 (dont 3 Unilu)

Nombre de téléchargements

0 (dont 0 Unilu)

Voir plus de statistiques

citations Scopus^®

citations Scopus^®
sans auto-citations

OpenCitations

citations OpenAlex

citations WoS^™

Bibliographie

Boukerche, Azzedine, Sha, Mingzhi, Design guidelines on deep learning–based pedestrian detection methods for supporting autonomous vehicles. ACM Comp. Surveys (CSUR) 54:6 (2021), 1–36 ACM New York, NY, USA.
Sha, Mingzhi, Boukerche, Azzedine, Performance evaluation of CNN-based pedestrian detectors for autonomous vehicles. Ad Hoc Netw., 128, 2022, 102784 Elsevier.
Kim, Sangjun, Kwak, Sooyeong, Ko, Byoung Chul, Fast pedestrian detection in surveillance video based on soft target training of shallow random forest. IEEE Access 7 (2019), 12415–12426 IEEE.
Oluyide, Oluwakorede Monica, Tapamo, Jules-Raymond, Walingo, Tom Mmbasu, Automatic dynamic range adjustment for pedestrian detection in thermal (Infrared) surveillance videos. Sensors, 22(5), 2022, 1728 MDPI.
Oltean, Gabriel, Ivanciu, Laura, Balea, Horea, Pedestrian detection and behaviour characterization for video surveillance systems. 2019 IEEE 25th International Symposium for Design and Technology in Electronic Packaging (SIITME), 2019, IEEE, 256–259.
Zou, Meiyuan, Jiajie, Yu, Bo, Lu, Chi, Wenzheng, Sun, Lining, Active pedestrian detection for excavator robots based on multi-sensor fusion. 2022 IEEE International Conference on Real-Time Computing and Robotics (RCAR), 2022, IEEE, 255–260.
Zhao, Zhe, Qi, Xianyu, Zhao, Yufei, Zhang, Jiadong, Wang, Wei, Yang, Xiangdong, Pedestrian Detection and Tracking Based on 2D Lidar and RGB-D Camera. Proceedings of the 2022 3rd International Conference on Control, Robotics and Intelligent System, 7–14, 2022.
Pang, Lei, Cao, Zhiqiang, Junzhi, Yu, Liang, Shuang, Chen, Xuechao, Zhang, Weimin, An efficient 3D pedestrian detector with calibrated RGB camera and 3D LiDAR. 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2019, IEEE, 2902–2907.
Gawande, Ujwalla, Hajari, Kamal, Golhar, Yogesh, Pedestrian Detection and Tracking in Video Surveillance System: Issues, Comprehensive Review, and Challenges, Recent Trends in Computational Intelligence. 2020, IntechOpen, 1–24.
Wang, Wenguang, Chang, Xiyuan, Yang, Jihuang, Xu, Gaofei, LiDAR-based dense pedestrian detection and tracking. Appl. Sci., 12(4), 2022, 1799 MDPI.
Ghari, Bahareh, Tourani, Ali, Asadollah, Shahbahrami, A robust pedestrian detection approach for autonomous vehicles. Proceedings of the 2022 8th Iranian Conference on Signal Processing and Intelligent Systems, 2022, IEEE, 1–5.
Yu, Jiang, Tong, Guoxiang, Yin, Henan, Xiong, Naixue, A pedestrian detection method based on genetic algorithm for optimize XGBoost training parameters. IEEE Access 7 (2019), 118310–118321 IEEE.
Li, Guofa, Yang, Yifan, Xingda, Qu, Deep learning approaches on pedestrian detection in hazy weather. IEEE Trans. Ind. Electron. 67:10 (2019), 8889–8899 IEEE.
Barba-Guaman, Luis, Naranjo, Jose Eugenio, Ortiz, Anthony, Deep learning framework for vehicle and pedestrian detection in rural roads on an embedded GPU. Electronics, 9(4), 2020, 589 MDPI.
Hung, Goon Li, Sahimi, Mohamad Safwan Bin, Samma, Hussein, Almohamad, Tarik Adnan, Lahasan, Badr, Faster R-CNN deep learning model for pedestrian detection from drone images. SN Comp. Sci. 1 (2020), 1–9 Springer.
Ahmed, Zahid, Iniyavan, R., et al. Enhanced vulnerable pedestrian detection using deep learning. 2019 International Conference on Communication and Signal Processing (ICCSP), 2019, IEEE, 0971–0974.
Chen, Long, Lin, Shaobo, Lu, Xiankai, Cao, Dongpu, Wu, Hangbin, Guo, Chi, Liu, Chun, Wang, Fei-Yue, Deep neural network based vehicle and pedestrian detection for autonomous driving: a survey. IEEE Trans. Intell. Transp. Syst. 22:6 (2021), 3234–3246 IEEE.
Geiger, Andreas, Lenz, Philip, Urtasun, Raquel, Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite. Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
Hou, Ya-Li, Song, Yaoyao, Hao, Xiaoli, Shen, Yan, Qian, Manyi, Chen, Houjin, Multispectral pedestrian detection based on deep convolutional neural networks. Infrared Phys. Technol. 94 (2018), 69–77 Elsevier.
Iftikhar, Sundas, Zhang, Zuping, Asim, Muhammad, Muthanna, Ammar, Koucheryavy, Andrey, Abd El-Latif, Ahmed A., Deep learning-based pedestrian detection in autonomous vehicles: substantial issues and challenges. Electronics, 11(21), 2022, 3551 MDPI.
Chen, Wei, Zhu, Yuxuan, Tian, Zijian, Zhang, Fan, Yao, Minda, Occlusion and multi-scale pedestrian detection: a review. Array, 2023, 100318 Elsevier.
Li, Fang, Li, Xueyuan, Liu, Qi, Li, Zirui, Occlusion handling and multi-scale pedestrian detection based on deep learning: a review. IEEE Access 10 (2022), 19937–19957 IEEE.
Davis, James W., Keck, Mark A., A two-stage template approach to person detection in thermal imagery. 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION’05)-Volume 1, 1, 2005, IEEE, 364–369.
Torabi, Atousa, Massé, Guillaume, Bilodeau, Guillaume-Alexandre, An iterative integrated framework for thermal–visible image registration, sensor fusion, and people tracking for video surveillance applications. Comput. Vis. Image Underst. 116:2 (2012), 210–221 Elsevier.
Socarrás, Yainuvis, Ramos, Sebastian, Vázquez, David, López, Antonio M., Theo Gevers, adapting pedestrian detection from synthetic to far infrared images. ICCV Workshops, 3, 2013.
Olmeda, Daniel, Premebida, Cristiano, Nunes, Urbano, Armingol, Jose Maria, de la Escalera, Arturo, Pedestrian detection in far infrared images. Integrat. Comp. Aided Eng. 20:4 (2013), 347–360 IOS Press.
Wu, Zheng, Fuller, Nathan, Theriault, Diane, Betke, Margrit, A thermal infrared video benchmark for visual analysis. Proc. IEEE Conf. Comp. Vision Pattern Recog. Workshops, 201–208, 2014.
Hwang, Soonmin, Park, Jaesik, Kim, Namil, Choi, Yukyung, Kweon, In So, Multispectral pedestrian detection: Benchmark dataset and baseline. Proceedings of the IEEE conference on computer vision and pattern recognition, 1037–1045, 2015.
Karol, Piniarski, Pawel, Pawlowski, Adam, Dabrowski, Video processing algorithms for detection of pedestrians. CMST 21:3 (2015), 141–150 PSNC, Poznan Supercomputing and Networking Center.
González, Alejandro, Fang, Zhijie, Socarras, Yainuvis, Serrat, Joan, Vázquez, David, Xu, Jiaolong, López, Antonio M., Pedestrian detection at day/night time with visible and FIR cameras: a comparison. Sensors, 16(6), 2016, 820 MDPI.
Jeong, Mira, Ko, Byoung Chul, Nam, Jae-Yeal, Early detection of sudden pedestrian crossing for safe driving during summer nights. IEEE Trans. Circuits Syst. Video Technol. 27:6 (2016), 1368–1380 IEEE.
Takumi, Karasawa, Watanabe, Kohei, Ha, Qishen, Tejero-De-Pablos, Antonio, Ushiku, Yoshitaka, Harada, Tatsuya, Multispectral object detection for autonomous vehicles. Proc. Thematic Workshops ACM Multimedia, 35–43, 2017, 2017.
Gebhardt, Evan, Wolf, Marilyn, Camel dataset for visual and thermal infrared multiple object detection and tracking. 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2018, IEEE, 1–6.
Neumann, Lukáš, Karg, Michelle, Zhang, Shanshan, Scharfenberger, Christian, Piegert, Eric, Mistr, Sarah, Prokofyeva, Olga, Thiel, Robert, Vedaldi, Andrea, Zisserman, Andrew, et al. Nightowls: A pedestrians at night dataset. Computer Vision–ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2–6, 2018, Revised Selected Papers, Part I 14, 2019, Springer, 691–705.
Xu, Zhewei, Zhuang, Jiajun, Liu, Qiong, Zhou, Jingkai, Peng, Shaowu, Benchmarking a large-scale FIR dataset for on-road pedestrian detection. Infrared Phys. Technol. 96 (2019), 199–208 Elsevier.
Kim, Taehwan, Kim, Sungho, Pedestrian detection at night time in FIR domain: comprehensive study about temperature and brightness and new benchmark. Pattern Recogn. 79 (2018), 44–54 Elsevier.
FLIR Thermal Dataset for Algorithm Training, https://www.flir.in/oem/adas/adas-dataset-form/, TELEDYNE FLIR, TELEDYNE FLIR, 2021.
Tumas, Paulius, Nowosielski, Adam, Serackis, Arturas, Pedestrian detection in severe weather conditions. IEEE Access 8 (2020), 62775–62784 IEEE.
Jia, Xinyu, Zhu, Chuang, Li, Minzhen, Tang, Wenqi, Zhou, Wenli, LLVIP: A visible-infrared paired dataset for low-light vision. Proceedings of the IEEE/CVF International Conference on Computer Vision, 3496–3504, 2021.
Farooq, Muhammad Ali, Shariff, Waseem, Corcoran, Peter, Evaluation of Thermal Imaging on Embedded GPU Platforms for Application in Vehicular Assistance Systems, arXiv preprint. arXiv:2201.01661, 2022.
Nowosielski, Adam, Małecki, Kırzysztof, Forczmański, Paweł, Smoliński, Anton, Krzywicki, Kazimierz, Embedded night-vision system for pedestrian detection. IEEE Sensors J. 20:16 (2020), 9293–9304 IEEE.
Kim, JongBae, Pedestrian detection and distance estimation using thermal camera in night time. 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 2019, IEEE, 463–466.
Kim, Jong Bae, Detection of direction indicators on road surfaces using inverse perspective mapping and NN. J. Inf. Proc. Korean 4 (2015), 201–208.
Zhou, Dongmei, Qiu, Shi, Yang, Song, Xia, Kaijian, A pedestrian extraction algorithm based on a single infrared image. Infrared Phys. Technol., 105, 2020, 103236 Elsevier.
Khalifa, Anouar Ben, Alouani, Ihsen, Mahjoub, Mohamed Ali, Amara, Najoua Essoukri Ben, Pedestrian detection using a moving camera: A novel framework for foreground detection. Cogn. Syst. Res. 60 (2020), 77–96 Elsevier.
Shahzad, Ali Raza, Jalal, Ahmad, A smart surveillance system for pedestrian tracking and counting using template matching. 2021 International Conference on Robotics and Automation in Industry (ICRAI), 2021, IEEE, 1–6.
Cai, Yingfeng, Liu, Ze, Wang, Hai, Sun, Xiaoqiang, Saliency-based pedestrian detection in far infrared images. IEEE Access 5 (2017), 5013–5019 IEEE.
Zhang, Hao, Xu, Han, Tian, Xin, Jiang, Junjun, Ma, Jiayi, Image fusion meets deep learning: a survey and perspective. Inform. Fusion 76 (2021), 323–336 Elsevier.
Nataprawira, Jason, Yanlei, Gu, Goncharenko, Igor, Kamijo, Shunsuke, Pedestrian detection on multispectral images in different lighting conditions. 2021 IEEE International Conference on Consumer Electronics (ICCE), 2021, IEEE, 1–5.
Redmon, Joseph, Ali Farhadi, Yolov3: An incremental improvement, arXiv preprint. arXiv:1804.02767, 2018.
Nataprawira, Jason, Yanlei, Gu, Goncharenko, Igor, Kamijo, Shunsuke, Pedestrian detection using multispectral images and a deep neural network. Sensors, 21(7), 2021, 2536 Multidisciplinary Digital Publishing Institute.
Yang, Yang, Xu, Kaixiong, Wang, Kaizheng, Cascaded information enhancement and cross-modal attention feature fusion for multispectral pedestrian detection. Front. Phys., 11, 2023, 1121311 Frontiers.
Zhang, Yongtao, Yin, Zhishuai, Nie, Linzhen, Huang, Song, Attention based multi-layer fusion of multispectral images for pedestrian detection. IEEE Access 8 (2020), 165071–165084 IEEE.
Zhang, Heng, Fromont, Elisa, Lefevre, Sébastien, Avignon, Bruno, Multispectral fusion for object detection with cyclic fuse-and-refine blocks. 2020 IEEE International Conference on Image Processing (ICIP), 2020, IEEE, 276–280.
Jiang, Qunyan, Dai, Juying, Rui, Ting, Shao, Faming, Wang, Jinkang, Lu, Guanlin, Attention-based cross-modality feature complementation for multispectral pedestrian detection. IEEE Access 10 (2022), 53797–53809 IEEE.
Lei, Fu, Wen-bin, Gu, Ai, Yong-bao, Li, Wei, Wang, Dong, Adaptive spatial pixel-level feature fusion network for multispectral pedestrian detection. Infrared Phys. Technol., 116, 2021, 103770 Elsevier.
Deng, Qing, Tian, Wei, Yuyao Huang, Lu, Xiong, Xin Bi, Pedestrian detection by Fusion of RGB and infrared images in low-light environment. 2021 IEEE 24th International Conference on Information Fusion (FUSION), 2021, IEEE, 1–8.
Liu, Jingjing, Zhang, Shaoting, Wang, Shu, Metaxas, Dimitris N., Multispectral deep neural networks for pedestrian detection, arXiv preprint. arXiv:1611.02644, 2016.
Cao, Yue, Bin, Junchi, Hamari, Jozsef, Blasch, Erik, Zheng, Liu, Multimodal object detection by channel switching and spatial attention. Proc. IEEE/CVF Conf. Comp. Vision Pattern Recog., 403–411, 2023.
Yang, Xiaoxiao, Qian, Yeqiang, Zhu, Huijie, Wang, Chunxiang, Yang, Ming, BAANet: Learning bi-directional adaptive attention gates for multispectral pedestrian detection. 2022 International Conference on Robotics and Automation (ICRA), 2022, IEEE, 2920–2926.
Zhang, Heng, Fromont, Elisa, Lefèvre, Sébastien, Avignon, Bruno, Guided attentive feature fusion for multispectral pedestrian detection. Proc. IEEE/CVF Winter Conf. Appl. Comp. Vision, 72–80, 2021.
Qingyun, Fang, Dapeng, Han, Zhaokui, Wang, Cross-modality fusion transformer for multispectral object detection, arXiv preprint. arXiv:2111.00273, 2021.
Pei, Dashun, Jing, Mingxuan, Liu, Huaping, Sun, Fuchun, Jiang, Linhua, A fast RetinaNet fusion framework for multi-spectral pedestrian detection. Infrared Phys. Technol., 105, 2020, 103178 Elsevier.
Lin, Tsung-Yi, Goyal, Priya, Girshick, Ross, He, Kaiming, Dollár, Piotr, Focal loss for dense object detection. Proc. IEEE Int. Conf. Comp. Vision, 2980–2988, 2017.
Lu, Ding, Wang, Yong, Laganiere, Robert, Huang, Dan, Fu, Shan, Convolutional neural networks for multispectral pedestrian detection. Signal Process. Image Commun., 82, 2020, 115764 Elsevier.
Dai, Jifeng, Li, Yi, He, Kaiming, Sun, Jian, R-FCN: object detection via region-based fully convolutional networks. Adv. Neural Inf. Proces. Syst., 29, 2016.
Yun, Jun-Seok, Park, Seon-Hoo, Yoo, Seok Bong, Infusion-Net: inter-and intra-weighted cross-fusion network for multispectral object detection. Mathematics, 10(21), 2022, 3966 MDPI.
Bao, Chun, Cao, Jie, Hao, Qun, Yang, Cheng, Ning, Yaqian, Zhao, Tianhua, Dual-YOLO architecture from infrared and visible images for object detection. Sensors, 23(6), 2023, 2934 MDPI.
Wang, Chien-Yao, Bochkovskiy, Alexey, Liao, Hong-Yuan Mark, YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 7464–7475, 2023.
Zhou, Xingyi, Wang, Dequan, Krähenbühl, Philipp, Objects as points. arXiv 2019, arXiv preprint. arXiv:1904.07850, 448, 1904.
Zuo, Xin, Wang, Zhi, Shen, Jifeng, Yang, Wankou, Improving multispectral pedestrian detection with scale-aware permutation attention and adjacent feature aggregation. IET Comput. 17 (2022), 726–738 Wiley Online Library.
Cao, Yanpeng, Guan, Dayan, Wu, Yulun, Yang, Jiangxin, Cao, Yanlong, Yang, Michael Ying, Box-level segmentation supervised deep neural networks for accurate and real-time multispectral pedestrian detection. ISPRS J. Photogramm. Remote Sens. 150 (2019), 70–79 Elsevier.
Tang, Linfeng, Yuan, Jiteng, Zhang, Hao, Jiang, Xingyu, Ma, Jiayi, PIAFusion: a progressive infrared and visible image fusion network based on illumination aware. Inform. Fusion 83 (2022), 79–92 Elsevier.
Roszyk, Kamil, Michał, Nowicki, R., Skrzypczyński, Piotr, Adopting the YOLOv4 architecture for low-latency multispectral pedestrian detection in autonomous driving. Sensors, 22(3), 2022, 1082 MDPI.
Peng, Peiran, Xu, Tingfa, Huang, Bo, Li, Jianan, HAFNet: hierarchical attentive Fusion network for multispectral pedestrian detection. Remote Sens., 15(8), 2023, 2041 MDPI.
Ravi, Yadav, Samir, Ahmed, Rashed, Hazem, Yogamani, Senthil, Dahyot, Rozenn, CNN based color and thermal image fusion for object detection in automated driving. Irish Machine Vision Image Proc.(2), 2020.
Ren, Shaoqing, He, Kaiming, Girshick, Ross, Sun, Jian, Faster R-CNN: towards real-time object detection with region proposal networks. Adv. Neural Inf. Proces. Syst., 28, 2015.
Lu, Zhang, Liu, Zhiyong, Zhang, Shifeng, Yang, Xu, Qiao, Hong, Huang, Kaizhu, Hussain, Amir, Cross-modality interactive attention network for multispectral pedestrian detection. Inform. Fusion 50 (2019), 20–29 Elsevier.
Hu, Ruizhe, Rui, Ting, Ouyang, Yan, Wang, Jinkang, Jiang, Qunyan, Du, Yinan, DMFFNet: dual-mode multi-scale feature fusion-based pedestrian detection method. IEEE Access, 2022 1–1 IEEE.
Howard, Andrew, Sandler, Mark, Chu, Grace, Chen, Liang-Chieh, Chen, Bo, Tan, Mingxing, Wang, Weijun, Zhu, Yukun, Pang, Ruoming, Vasudevan, Vijay, et al. Searching for MobileNetV3. Proceedings of the IEEE/CVF International Conference on Computer Vision, 1314–1324, 2019.
Cao, Zhiwei, Yang, Huihua, Zhao, Juan, Guo, Shuhong, Li, Lingqiao, Attention fusion for one-stage multispectral pedestrian detection. Sensors, 21(12), 2021, 4184 MDPI.
Kim, Jaekyum, Choi, Jaehyung, Kim, Yechol, Koh, Junho, Chung, Chung Choo, Choi, Jun Won, Robust camera lidar sensor fusion via deep gated information fusion network. 2018 IEEE Intelligent Vehicles Symposium (IV), 2018, IEEE, 1620–1625.
Yang, Zheng, Izzat, Izzat H., Ziaee, Shahrzad, GFD-SSD: gated fusion double SSD for multispectral pedestrian detection, arXiv preprint. arXiv:1903.06999, 2019.
Liu, Wei, Anguelov, Dragomir, Erhan, Dumitru, Szegedy, Christian, Reed, Scott, Cheng-Yang, Fu, Berg, Alexander C., SSD: Single Shot Multibox Detector, Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, 21–37. 2016, Springer.
Wang, Qingwang, Chi, Yongke, Shen, Tao, Song, Jian, Zhang, Zifeng, Zhu, Yan, Improving RGB-infrared object detection by reducing cross-modality redundancy. Remote Sens., 14(9), 2020, 2022 MDPI.
Xie, Qian, Cheng, Ta-Ying, Zhong, Jia-Xing, Zhou, Kaichen, Markham, Andrew, Trigoni, Niki, Fusion, Beyond, Modality hallucination-based multispectral fusion for pedestrian detection. Proc. IEEE/CVF Winter Conf. Appl. Comp. Vision, 655–664, 2024.
Lee, Sangin, Kim, Taejoo, Shin, Jeongmin, Kim, Namil, Choi, Yukyung, INSANet: INtra-INter spectral attention network for effective feature fusion of multispectral pedestrian detection. Sensors, 24(4), 2024, 1168 MDPI.
Xing, Yinghui, Wang, Song, Liang, Guoqiang, Li, Qingyi, Zhang, Xiuwei, Zhang, Shizhou, Zhang, Yanning, Multispectral Pedestrian Detection via Reference Box Constrained Cross Attention and Modality Balanced Optimization, arXiv preprint. arXiv:2302.00290, 2023.
Khalid, Bushra, Khan, Asad Mansoor, Akram, Muhammad Usman, Batool, Sherin, Person detection by fusion of visible and thermal images using convolutional neural network. 2019 2nd International Conference on Communication, Computing and Digital Systems (C-CODE), 2019, IEEE, 143–148.
Montenegro, Bryan, Flores-Calero, Marco, Pedestrian detection at daytime and nighttime conditions based on YOLO-v5. Ingenius. Rev. Ciencia Tecnol. 27 (2022), 85–95.
Song, Xiaoru, Gao, Song, Chen, Chaobo, A multispectral feature fusion network for robust pedestrian detection. Alexandria Eng. J. 60:1 (2021), 73–85 Elsevier.
Lu, Ding, Wang, Yong, Laganiere, Robert, Huang, Dan, Luo, Xinbin, Zhang, Huanlong, A robust and fast multispectral pedestrian detection deep network. Knowl.-Based Syst., 227, 2021, 106990 Elsevier.
Park, Kihong, Kim, Seungryong, Sohn, Kwanghoon, Unified multi-spectral pedestrian detection based on probabilistic fusion networks. Pattern Recogn. 80 (2018), 143–155 Elsevier.
Bochkovskiy, Alexey, Wang, Chien-Yao, Liao, Hong-Yuan Mark, Yolov4: Optimal speed and accuracy of object detection, arXiv preprint. arXiv:2004.10934, 2020.
Chan, Hung-Tse, Tsai, Po-Ting, Hsia, Chih-Hsien, Multispectral pedestrian detection via two-stream YOLO with complementarity Fusion for autonomous driving. 2023 IEEE 3rd International Conference on Electronic Communications, Internet of Things and Big Data (ICEIB), 2023, IEEE, 313–316.
Chen, Yi-Ting, Shi, Jinghao, Ye, Zelin, Mertz, Christoph, Ramanan, Deva, Kong, Shu, Multimodal object detection via probabilistic ensembling. European Conference on Computer Vision, 2022, Springer, 139–158.
Shaikh, Zuhaib Ahmed, Van Hamme, David, Veelaert, Peter, Philips, Wilfried, Probabilistic fusion for pedestrian detection from thermal and colour images. Sensors, 22(22), 2022, 8637 MDPI.
Zhuang, Yifan, Ziyuan, Pu, Hu, Jia, Wang, Yinhai, Illumination and temperature-aware multispectral networks for edge-computing-enabled pedestrian detection. IEEE Trans. Network Sci. Eng. 9:3 (2021), 1282–1295 IEEE.
Hsia, Chih-Hsien, Peng, Hsiao-Chu, Chan, Hung-Tse, All-weather pedestrian detection based on double-stream multispectral network. Electronics, 12(10), 2023, 2312 MDPI.
Li, Chengyang, Song, Dan, Tong, Ruofeng, Tang, Min, Illumination-aware faster R-CNN for robust multispectral pedestrian detection. Pattern Recogn. 85 (2019), 161–171 Elsevier.
Li, Guofa, Lai, Weijian, Xingda, Qu, Pedestrian detection based on light perception fusion of visible and thermal images. Opt. Laser Technol., 156, 2022, 108466 Elsevier.
Redmon, Joseph, Divvala, Santosh, Girshick, Ross, Farhadi, Ali, Once, You Only Look, Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 779–788, 2016.
Cao, Yanpeng, Luo, Xing, Yang, Jiangxin, Cao, Yanlong, Yang, Michael Ying, Locality guided cross-modal feature aggregation and pixel-level fusion for multispectral pedestrian detection. Inform. Fusion 88 (2022), 1–11 Elsevier.
Wolpert, Alexander, Teutsch, Michael, Sarfraz, M. Saquib, Stiefelhagen, Rainer, Anchor-free small-scale multispectral pedestrian detection, arXiv preprint. arXiv:2008.08418, 2020.
Kim, Taeheon, Chung, Sangyun, Yeom, Damin, Youngjoon, Yu, Kim, Hak Gu, Ro, Yong Man, MSCoTDet: Language-driven Multi-modal Fusion for Improved Multispectral Pedestrian Detection, arXiv preprint. arXiv:2403.15209, 2024.
Jinda, Hu, Zhao, Yanshun, Zhang, Xindong, Application of transfer learning in infrared pedestrian detection. 2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC), 2020, IEEE, 1–4.
Zhu, Jun-Yan, Park, Taesung, Isola, Phillip, Efros, Alexei A., Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE international conference on computer vision, 2223–2232, 2017.
Vandersteegen, Maarten, Van Beeck, Kristof, Goedemé, Toon, Real-time multispectral pedestrian detection with a single-pass deep neural network. Image Analysis and Recognition: 15th International Conference, ICIAR 2018, Póvoa de Varzim, Portugal, June 27–29, 2018, Proceedings, 15, 2018, Springer, 419–426.
Redmon, Joseph, Farhadi, Ali, YOLO9000: Better, faster, stronger. Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 7263–7271, 2017.
Geng, Shengqi, Infrared image pedestrian target detection based on yolov3 and migration learning, arXiv preprint. arXiv:2012.11185, 2020.
Zheng, Zhaohui, Wang, Ping, Liu, Wei, Li, Jinze, Ye, Rongguang, Ren, Dongwei, Distance-IoU loss: faster and better learning for bounding box regression. Proc. AAAI Conf. Artific. Intel. 34:07 (2020), 12993–13000.
Guo, Tiantong, Huynh, Cong Phuoc, Solh, Mashhour, Domain-adaptive pedestrian detection in thermal images. 2019 IEEE International Conference on Image Processing (ICIP), 2019, IEEE, 1660–1664.
Kieu, My, Bagdanov, Andrew D., Bertini, Marco, Del Bimbo, Alberto, Task-conditioned domain adaptation for pedestrian detection in thermal imagery. Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXII 16, 546–562, 2020, Springer.
Kieu, My, Bagdanov, Andrew D., Bertini, Marco, Del Bimbo, Alberto, Domain adaptation for privacy-preserving pedestrian detection in thermal imagery. Image Analysis and Processing–ICIAP 2019: 20th International Conference, Trento, Italy, September 9–13, 2019, Proceedings, Part II 20, 2019, Springer, 203–213.
Kieu, My, Bagdanov, Andrew D., Bertini, Marco, Bottom-up and layerwise domain adaptation for pedestrian detection in thermal images. ACM Trans. Multimedia Comp. Commun. Appl. (TOMM) 17:1 (2021), 1–19 ACM New York, NY.
Krišto, Mate, Ivasic-Kos, Marina, Pobar, Miran, Thermal object detection in difficult weather conditions using YOLO. IEEE Access 8 (2020), 125459–125476 IEEE.
Fritz, Kevin, König, Daniel, Klauck, Ulrich, Teutsch, Michael, Generalization ability of region proposal networks for multispectral person detection. Automat. Target Recog. XXIX 10988 (2019), 222–235 SPIE.
Dollar, Piotr, Wojek, Christian, Schiele, Bernt, Perona, Pietro, Pedestrian detection: an evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 34:4 (2011), 743–761 IEEE.
Zhang, Shanshan, Benenson, Rodrigo, Schiele, Bernt, Citypersons: a diverse dataset for pedestrian detection. Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 3213–3221, 2017.
Ha, Qishen, Watanabe, Kohei, Karasawa, Takumi, Ushiku, Yoshitaka, Harada, Tatsuya, MFNet: Towards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes. 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017, IEEE, 5108–5115.
Vs, Vibashan, Poster, Domenick, You, Suya, Hu, Shuowen, Patel, Vishal M., Meta-uda: Unsupervised domain adaptive thermal object detection using meta-learning. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 1412–1423, 2022.
Lyu, Chengjin, Heyer, Patrick, Munir, Asad, Platisa, Ljiljana, Micheloni, Christian, Goossens, Bart, Philips, Wilfried, Visible-Thermal pedestrian detection via unsupervised transfer learning. 2021 the 5th International Conference on Innovation in Artificial Intelligence, 2021, 158–163.
Munir, Farzeen, Azam, Shoaib, Jeon, Moongu, Sstn: self-supervised domain adaptation thermal object detection for autonomous driving. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021, IEEE, 206–213.
Lyu, Chengjin, Heyer, Patrick, Goossens, Bart, Philips, Wilfried, An unsupervised transfer learning framework for visible-thermal pedestrian detection. Sensors, 22(12), 2022, 4416 Mdpi.
Cao, Yanpeng, Guan, Dayan, Huang, Weilin, Yang, Jiangxin, Cao, Yanlong, Yu, Qiao, Pedestrian detection with unsupervised multispectral feature learning using deep neural networks. Inform. Fusion 46 (2019), 206–217 Elsevier.
Guan, Dayan, Luo, Xing, Cao, Yanpeng, Yang, Jiangxin, Cao, Yanlong, Vosselman, George, Yang, Michael Ying, Unsupervised domain adaptation for multispectral pedestrian detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 0–0, 2019.
Marnissi, Mohamed Amine, Fradi, Hajer, Sahbani, Anis, Amara, Najoua Essoukri Ben, Unsupervised thermal-to-visible domain adaptation method for pedestrian detection. Pattern Recogn. Lett. 153 (2022), 222–231 Elsevier.
Hnewa, Mazin, Rahimpour, Alireza, Miller, Justin, Upadhyay, Devesh, Hayder Radha, cross modality knowledge distillation for robust pedestrian detection in low light and adverse weather conditions. ICASSP 2023–2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023, IEEE, 1–5.
Hinton, Geoffrey, Vinyals, Oriol, Dean, Jeff, Distilling the knowledge in a neural network, arXiv preprint. arXiv:1503.02531, 2015.
Zhang, Heng, Fromont, Elisa, Lefèvre, Sébastien, Avignon, Bruno, Low-cost multispectral scene analysis with modality distillation. Proc. IEEE/CVF Winter Conf. Appl. Comp. Vision, 803–812, 2022.
Liu, Tianshan, Lam, Kin-Man, Zhao, Rui, Qiu, Guoping, Deep cross-modal representation learning and distillation for illumination-invariant pedestrian detection. IEEE Trans. Circuits Syst. Video Technol. 32:1 (2021), 315–329 IEEE.
Kim, Jung Uk, Park, Sungjune, Ro, Yong Man, Robust small-scale pedestrian detection with cued recall via memory learning. Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, 3050–3059.
Kim, Jung Uk, Park, Sungjune, Ro, Yong Man, Towards versatile pedestrian detector with multisensory-matching and multispectral recalling memory. Proc. AAAI Conf. Artif. Intell. 36:1 (2022), 1157–1165.
Marnissi, Mohamed Amine, Fradi, Hajer, Sahbani, Anis, Amara, Najoua Essoukri Ben, Thermal image enhancement using generative adversarial network for pedestrian detection. 2020 25th International Conference on Pattern Recognition (ICPR), 2021, IEEE, 6509–6516.
Sun, Yue, Shao, Yifeng, Yang, Guanglin, Xie, Haiyan, A method of infrared image pedestrian detection with improved YOLOv3 algorithm. Am. J. Optics Photon. 9:3 (2021), 32–38 Science Publishing Group.
Yu, Jiahui, Fan, Yuchen, Yang, Jianchao, Xu, Ning, Wang, Zhaowen, Wang, Xinchao, Huang, Thomas, Wide activation for efficient and accurate image super-resolution, arXiv preprint. arXiv:1808.08718, 2018.
Marnissi, Mohamed Amine, Fathallah, Abir, GAN-based vision transformer for high-quality thermal image enhancement. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, 817–825.
Tang, Linfeng, Xiang, Xinyu, Zhang, Hao, Gong, Meiqi, Ma, Jiayi, DIVFusion: darkness-free infrared and visible image fusion. Inform. Fusion 91 (2023), 477–493 Elsevier.
Li, Gang, Zhang, Shanshan, Yang, Jian, Nighttime pedestrian detection based on feature attention and transformation. 2020 25th International Conference on Pattern Recognition (ICPR), 2021, IEEE, 9180–9187.
Cui, Chenhang, Xie, Jinyu, Yang, Yechenhao, Bright Channel Prior Attention for Multispectral Pedestrian Detection, arXiv preprint. arXiv:2305.12845, 2023.
Chen, Yunfan, Shin, Hyunchul, Pedestrian detection at night in infrared images using an attention-guided encoder-decoder convolutional neural network. Appl. Sci., 10(3), 2020, 809 MDPI.
Patel, Heena, Prajapati, Kalpesh, Sarvaiya, Anjali, Upla, Kishor, Raja, Kiran, Ramachandra, Raghavendra, Busch, Christoph, Depthwise convolution for compact object detector in nighttime images. Proc. IEEE/CVF Conf. Comp. Vision Pattern Recog., 379–389, 2022.
Li, Shasha, Li, Yongjun, Li, Yao, Li, Mengjun, Xu, Xiaorong, Yolo-firi: improved yolov5 for infrared image object detection. IEEE Access 9 (2021), 141861–141875 IEEE.
Li, Hui, Wu, Xiao-Jun, DenseFuse: a fusion approach to infrared and visible images. IEEE Trans. Image Process. 28:5 (2018), 2614–2623 IEEE.
Pang, Yingxue, Lin, Jianxin, Qin, Tao, Chen, Zhibo, Image-to-image translation: methods and applications. IEEE Trans. Multimedia 24 (2021), 3859–3881 IEEE.
Luo, Fuya, Li, Yunhan, Zeng, Guang, Peng, Peng, Wang, Gang, Li, Yongjie, Thermal infrared image colorization for nighttime driving scenes with top-down guided attention. IEEE Trans. Intell. Transp. Syst. 23:9 (2022), 15808–15823 IEEE.
Dangle, Anagha, Mundada, Radhika, Gore, Sonal, Shrungare, Jeenisha, Dalal, Harish, Enhanced colorization of thermal images for pedestrian detection using deep convolutional neural networks. Proc. Comp. Sci. 218 (2023), 2091–2101 Elsevier.
Yang, Shihao, Sun, Min, Lou, Xiayin, Yang, Hanjun, Zhou, Hang, An unpaired thermal infrared image translation method using GMA-CycleGAN. Remote Sens., 15(3), 2023, 663 MDPI.
Devaguptapu, Chaitanya, Akolekar, Ninad, Sharma, Manuj M., Balasubramanian, Vineeth N., Borrow from anywhere: Pseudo multi-modal object detection in thermal imagery. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 0–0, 2019.
Kieu, My, Berlincioni, Lorenzo, Galteri, Leonardo, Bertini, Marco, Bagdanov, Andrew D., Del Bimbo, Alberto, Robust pedestrian detection in thermal imagery using synthesized images. 2020 25th International Conference on Pattern Recognition (ICPR), 2021, IEEE, 8804–8811.
Mao, Xudong, Li, Qing, Xie, Haoran, Lau, Raymond Y.K., Wang, Zhen, Multi-class generative adversarial networks with the L2 loss function, arXiv preprint. arXiv:1611.04076.
Altay, Fatih, Velipasalar, Senem, The use of thermal cameras for pedestrian detection. IEEE Sensors J. 22:12 (2022), 11489–11498 IEEE.
Ghose, Debasmita, Desai, Shasvat M., Bhattacharya, Sneha, Chakraborty, Deep, Fiterau, Madalina, Rahman, Tauhidur, Pedestrian detection in thermal images using saliency maps. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 0–0, 2019.
Liu, Nian, Han, Junwei, Yang, Ming-Hsuan, Picanet: learning pixel-wise contextual attention for saliency detection. Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 3089–3098, 2018.
Deng, Zijun, Hu, Xiaowei, Zhu, Lei, Xu, Xuemiao, Qin, Jing, Han, Guoqiang, Heng, Pheng-Ann, R3net: Recurrent residual refinement network for saliency detection. Proceedings of the 27th International Joint Conference on Artificial Intelligence, 684–690, 2018 AAAI Press Menlo Park, CA, USA.
Marnissi, Mohamed Amine, Hattab, Ikram, Fradi, Hajer, Sahbani, Anis, Amara, Najoua Essoukri Ben, Bispectral Pedestrian Detection Augmented with Saliency Maps using Transformer, VISIGRAPP (5: VISAPP). 2022, 275–284.
Zhao, Yifan, Cheng, Jingchun, Zhou, Wei, Zhang, Chunxi, Pan, Xiong, Infrared pedestrian detection with converted temperature map. 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2019, IEEE, 2025–2031.
Oksuz, Kemal, Cam, Baris Can, Kalkan, Sinan, Akbas, Emre, Imbalance problems in object detection: a review. IEEE Trans. Pattern Anal. Mach. Intell. 43:10 (2020), 3388–3415 IEEE.
Zhou, Kailai, Chen, Linsen, Cao, Xun, Improving multispectral pedestrian detection by addressing modality imbalance problems. Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XVIII 16, 2020, Springer, 787–803.
Das, Arindam, Das, Sudip, Sistu, Ganesh, Horgan, Jonathan, Bhattacharya, Ujjwal, Jones, Edward, Glavin, Martin, Revisiting Modality Imbalance In Multimodal Pedestrian Detection, arXiv preprint arXiv:2302.12589. 2023.
Gross, Leonard, Logarithmic sobolev inequalities. Am. J. Math. 97:4 (1975), 1061–1083 JSTOR.
Li, Wei, Infrared image pedestrian detection via YOLO-V3. 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 5, 2021, IEEE, 1052–1055.
Dasgupta, Kinjal, Das, Arindam, Das, Sudip, Bhattacharya, Ujjwal, Yogamani, Senthil, Spatio-contextual deep network-based multimodal pedestrian detection for autonomous driving. IEEE Trans. Intell. Transp. Syst. 23:9 (2022), 15940–15950 IEEE.
Velickovic, Petar, Cucurull, Guillem, Casanova, Arantxa, Romero, Adriana, Lio, Pietro, Bengio, Yoshua, et al. Graph attention networks. stat 1050:20 (2017), 10–48550.
Bell, Sean, Zitnick, C. Lawrence, Bala, Kavita, Girshick, Ross, Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2874–2883, 2016.
Kim, Taeheon, Shin, Sebin, Yu, Youngjoon, Kim, Hak Gu, Ro, Yong Man, Causal Mode Multiplexer: A Novel Framework for Unbiased Multispectral Pedestrian Detection, arXiv preprint. arXiv:2403.01300, 2024.
Lu, Zhang, Zhu, Xiangyu, Chen, Xiangyu, Xu, Yang, Lei, Zhen, Liu, Zhiyong, Weakly aligned cross-modal learning for multispectral pedestrian detection. Proc. IEEE/CVF Int. Conf. Comp. Vision, 5127–5137, 2019.
Kim, Minsu, Joung, Sunghun, Park, Kihong, Kim, Seungryong, Sohn, Kwanghoon, Unpaired cross-spectral pedestrian detection via adversarial feature learning. 2019 IEEE International Conference on Image Processing (ICIP), 2019, IEEE, 1650–1654.
Kim, Jung Uk, Park, Sungjune, Ro, Yong Man, Uncertainty-guided cross-modal learning for robust multispectral pedestrian detection. IEEE Trans. Circuits Syst. Video Technol. 32:3 (2021), 1510–1523 IEEE.
Wanchaitanawong, Napat, Tanaka, Masayuki, Shibata, Takashi, Okutomi, Masatoshi, Multi-modal pedestrian detection with large misalignment based on modal-wise regression and multi-modal IoU. 2021 17th International Conference on Machine Vision and Applications (MVA), 2021, IEEE, 1–6.
Yue, Jun, Fang, Leyuan, Shaobo, Xia, Yue, Deng, Ma, Jiayi, Dif-fusion: towards high color fidelity in infrared and visible image fusion with diffusion models. IEEE Trans. Image Process. 32 (2023), 5705–5720 IEEE.
Zhao, Zixiang, Bai, Haowen, Zhu, Yuanzhi, Zhang, Jiangshe, Shuang, Xu, Zhang, Yulun, Zhang, Kai, Meng, Deyu, Timofte, Radu, Van Gool, Luc, DDFM: Denoising diffusion model for multi-modality image fusion. Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, 8082–8093.
Li, Ping, Chen, Junjie, Lin, Binbin, Xu, Xianghua, Residual spatial fusion network for rgb-thermal semantic segmentation, arXiv preprint. arXiv:2306.10364, 2023.
Wang, Hai, Xu, Yansong, He, Youguo, Cai, Yingfeng, Chen, Long, Li, Yicheng, Sotelo, Miguel Angel, Li, Zhixiong, YOLOv5-Fog: A multiobjective visual detection algorithm for fog driving scenes based on improved YOLOv5. IEEE Transactions on Instrumentation and Measurement, 71, 2022, IEEE, 1–12.
Song, Fangzheng, Li, Peng, YOLOv5-MS: Real-time multi-surveillance pedestrian target detection model for smart cities. Biomimetics, 8(6), 2023, 480 MDPI.
Chen, Yuanjie, Wang, Chunyuan, Zhang, Chi, A robust lightweight network for pedestrian detection based on YOLOv5-x. Appl. Sci., 13(18), 2023, 10225 MDPI.
Yi, Kefu, Luo, Kai, Chen, Tuo, Hu, Rongdong, An improved YOLOX model and domain transfer strategy for nighttime pedestrian and vehicle detection. Appl. Sci., 12(23), 2022, 12476 MDPI.
Nikolov, Ivan Adriyanov, Variational autoencoders for pedestrian synthetic data augmentation of existing datasets-a preliminary investigation. 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2024, SCITEPRESS Digital Library, 829–836.
Crawshaw, Michael, Multi-task learning with deep neural networks: A survey, arXiv preprint. arXiv:2009.09796, 2020.
Wang, Yuanzhi, Lu, Tao, Zhang, Yanduo, Fang, Wenhua, Wu, Yuntao, Wang, Zhongyuan, Cross-task feature alignment for seeing pedestrians in the dark. Neurocomputing 462 (2021), 282–293 Elsevier.
Guan, Dayan, Cao, Yanpeng, Yang, Jiangxin, Cao, Yanlong, Yang, Michael Ying, Fusion of multispectral data through illumination-aware deep neural networks for pedestrian detection. Inform. Fusion 50 (2019), 148–157 Elsevier.
Dai, Xiaobiao, Hu, Junping, Zhang, Hongmei, Shitu, Abubakar, Luo, Chunlei, Osman, Ahmad, Sfarra, Stefano, Multi-task Faster R-CNN for nighttime pedestrian detection and distance estimation. Infrared Phys. Technol., 115, 2021, 103694 Elsevier.
Cao, Zhiwei, Yang, Huihua, Zhao, Juan, Pan, Xipeng, Zhang, Longhao, Liu, Zhenbing, A new region proposal network for far-infrared pedestrian detection. IEEE Access 7 (2019), 135023–135030 IEEE.
Lin, Tsung-Yi, Dollár, Piotr, Girshick, Ross, He, Kaiming, Hariharan, Bharath, Belongie, Serge, Feature pyramid networks for object detection. Proceedings of the IEEE conference on computer vision and pattern recognition, 2117–2125, 2017.
Li, Chengyang, Song, Dan, Tong, Ruofeng, Tang, Min, Multispectral pedestrian detection via simultaneous detection and segmentation, arXiv preprint. arXiv:1808.04818, 2018.
Chen, Yung-Yao, Jhong, Sin-Ye, Li, Guan-Yi, Chen, Ping-Han, Thermal-based pedestrian detection using faster r-cnn and region decomposition branch. 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), 2019, IEEE, 1–2.
Liu, Wei, Liao, Shengcai, Ren, Weiqiang, Hu, Weidong, Yu, Yinan, High-level semantic feature detection: a new perspective for pedestrian detection. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 5187–5196, 2019.
Yu, Song, Li, Min, Qiu, Xiaohua, Du, Weidong, Feng, Jin, Full-time infrared feature pedestrian detection based on CSP network. 2020 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), 2020, IEEE, 516–518.
Xu, Zhewei, Vong, Chi-Man, Wong, Chi-Chong, Liu, Qiong, Ground plane context aggregation network for day-and-night on vehicular pedestrian detection. IEEE Trans. Intell. Transp. Syst. 22:10 (2020), 6395–6406 IEEE.
Dai, Xiaobiao, Duan, Yuxia, Hu, Junping, Liu, Shicai, Hu, Caiqi, He, Yunze, Chen, Dapeng, Luo, Chunlei, Meng, Jianqiao, Near infrared nighttime road pedestrians recognition based on convolutional neural network. Infrared Phys. Technol. 97 (2019), 25–32 Elsevier.
Bishop, Christopher M., Nasrabadi, Nasser M., Pattern Recognition and Machine Learning. 4, 2006, Springer, 4.
Galarza-Bravo, Michelle A., Flores-Calero, Marco J., Pedestrian detection at night based on Faster R-CNN and far infrared images. Intelligent Robotics and Applications: 11th International Conference, ICIRA 2018, Newcastle, NSW, Australia, August 9–11, 2018, Proceedings, Part II, 2018, Springer, 335–345.
Kalita, Rumi, Talukdar, Anjan Kumar, Sarma, Kandarpa Kumar, Real-time human detection with thermal camera feed using YOLOv3. 2020 IEEE 17th India Council International Conference (INDICON), 2020, IEEE, 1–5.
Chebrolu, Koti Naga Renu, Kumar, P.N., Deep learning based pedestrian detection at all light conditions. 2019 International Conference on Communication and Signal Processing (ICCSP), 2019, IEEE, 0838–0842.
Kim, Jong Hyun, Batchuluun, Ganbayar, Park, Kang Ryoung, Pedestrian detection based on faster R-CNN in nighttime by fusing deep convolutional features of successive images. Expert Systems with Applications, 114, 2018, 15–33 Elsevier.
Konig, Daniel, Adam, Michael, Jarvers, Christian, Layher, Georg, Neumann, Heiko, Teutsch, Michael, Fully convolutional region proposal networks for multispectral person detection. Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 49–56, 2017.
Tumas, Paulius, Jonkus, Artūras, Serackis, Artūras, Acceleration of HOG based pedestrian detection in FIR camera video stream. 2018 Open Conference of Electrical, Electronic and Information Sciences (eStream), 2018, IEEE, 1–4.
Narayanan, A., Darshan Kumar, R., RoselinKiruba, R., Sree Sharmila, T., Study and analysis of pedestrian detection in thermal images using YOLO and SVM. 2021 Sixth International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 2021, IEEE, 431–434.
Xu, Dan, Ouyang, Wanli, Ricci, Elisa, Wang, Xiaogang, Sebe, Nicu, Learning cross-modal deep representations for robust pedestrian detection. Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 5363–5371, 2017.
Dollár, Piotr, Appel, Ron, Belongie, Serge, Perona, Pietro, Fast feature pyramids for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 36:8 (2014), 1532–1545 IEEE.
Choi, Hangil, Kim, Seungryong, Park, Kihong, Sohn, Kwanghoon, Multi-spectral pedestrian detection based on accumulated object proposal with fully convolutional networks. 2016 23rd International Conference on Pattern Recognition (ICPR), 2016, IEEE, 621–626.
Xu, Chen, Liu, Lei, Tan, Xin, Robust pedestrian detection based on multi-spectral image fusion and convolutional neural networks. Electronics, 11(1), 2021, 1 MDPI.
Ma, Yong, Chen, Jun, Chen, Chen, Fan, Fan, Ma, Jiayi, Infrared and visible image fusion using total variation model. Neurocomputing 202 (2016), 12–19 Elsevier.