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See detail3D SPARSE DEFORMATION SIGNATURE FOR DYNAMIC FACE RECOGNITION
Shabayek, Abd El Rahman UL; Aouada, Djamila UL; Cherenkova, Kseniya UL et al

in 27th IEEE International Conference on Image Processing (ICIP 2020), Abu Dhabi 25-28 October 2020 (2020, October)

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See detailPVDeConv: Point-Voxel Deconvolution for Autoencoding CAD Construction in 3D
Cherenkova, Kseniya UL; Aouada, Djamila UL; Gusev, Gleb

Scientific Conference (2020, October)

We propose a Point-Voxel DeConvolution (PVDeConv) mod- ule for 3D data autoencoder. To demonstrate its efficiency we learn to synthesize high-resolution point clouds of 10k points that densely describe ... [more ▼]

We propose a Point-Voxel DeConvolution (PVDeConv) mod- ule for 3D data autoencoder. To demonstrate its efficiency we learn to synthesize high-resolution point clouds of 10k points that densely describe the underlying geometry of Computer Aided Design (CAD) models. Scanning artifacts, such as pro- trusions, missing parts, smoothed edges and holes, inevitably appear in real 3D scans of fabricated CAD objects. Learning the original CAD model construction from a 3D scan requires a ground truth to be available together with the corresponding 3D scan of an object. To solve the gap, we introduce a new dedicated dataset, the CC3D, containing 50k+ pairs of CAD models and their corresponding 3D meshes. This dataset is used to learn a convolutional autoencoder for point clouds sampled from the pairs of 3D scans - CAD models. The chal- lenges of this new dataset are demonstrated in comparison with other generative point cloud sampling models trained on ShapeNet. The CC3D autoencoder is efficient with respect to memory consumption and training time as compared to state- of-the-art models for 3D data generation. [less ▲]

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See detailGOING DEEPER WITH NEURAL NETWORKS WITHOUT SKIP CONNECTIONS
Oyedotun, Oyebade UL; Shabayek, Abd El Rahman UL; Aouada, Djamila UL et al

in IEEE International Conference on Image Processing (ICIP 2020), Abu Dhabi, UAE, Oct 25–28, 2020 (2020, May 30)

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See detail3D DEFORMATION SIGNATURE FOR DYNAMIC FACE RECOGNITION
Shabayek, Abd El Rahman UL; Aouada, Djamila UL; Cherenkova, Kseniya UL et al

in 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020), Barcelona 4-8 May 2020 (2020, May)

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See detailStructured Compression of Deep Neural Networks with Debiased Elastic Group LASSO
Oyedotun, Oyebade UL; Aouada, Djamila UL; Ottersten, Björn UL

in IEEE 2020 Winter Conference on Applications of Computer Vision (WACV 20), Aspen, Colorado, US, March 2–5, 2020 (2020, March 01)

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See detailTowards Automatic CAD Modeling from 3D Scan Sketch based Representation
Shabayek, Abd El Rahman UL; Aouada, Djamila UL; Cherenkova, Kseniya UL et al

in Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020), GRAPP (2020, February)

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See detailFast Adaptive Reparametrization (FAR) with Application to Human Action Recognition
Ghorbel, Enjie UL; Demisse, Girum UL; Aouada, Djamila UL et al

in IEEE Signal Processing Letters (2020)

In this paper, a fast approach for curve reparametrization, called Fast Adaptive Reparamterization (FAR), is introduced. Instead of computing an optimal matching between two curves such as Dynamic Time ... [more ▼]

In this paper, a fast approach for curve reparametrization, called Fast Adaptive Reparamterization (FAR), is introduced. Instead of computing an optimal matching between two curves such as Dynamic Time Warping (DTW) and elastic distance-based approaches, our method is applied to each curve independently, leading to linear computational complexity. It is based on a simple replacement of the curve parameter by a variable invariant under specific variations of reparametrization. The choice of this variable is heuristically made according to the application of interest. In addition to being fast, the proposed reparametrization can be applied not only to curves observed in Euclidean spaces but also to feature curves living in Riemannian spaces. To validate our approach, we apply it to the scenario of human action recognition using curves living in the Riemannian product Special Euclidean space SE(3) n. The obtained results on three benchmarks for human action recognition (MSRAction3D, Florence3D, and UTKinect) show that our approach competes with state-of-the-art methods in terms of accuracy and computational cost. [less ▲]

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See detailDeepVI: A Novel Framework for Learning Deep View-Invariant Human Action Representations using a Single RGB Camera
Papadopoulos, Konstantinos UL; Ghorbel, Enjie UL; Oyedotun, Oyebade UL et al

in IEEE International Conference on Automatic Face and Gesture Recognition, Buenos Aires 18-22 May 2020 (2020)

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See detailTemporal 3D Human Pose Estimation for Action Recognition from Arbitrary Viewpoints
Adel Musallam, Mohamed; Baptista, Renato UL; Al Ismaeil, Kassem UL et al

in 6th Annual Conf. on Computational Science & Computational Intelligence, Las Vegas 5-7 December 2019 (2019, December)

This work presents a new view-invariant action recognition system that is able to classify human actions by using a single RGB camera, including challenging camera viewpoints. Understanding actions from ... [more ▼]

This work presents a new view-invariant action recognition system that is able to classify human actions by using a single RGB camera, including challenging camera viewpoints. Understanding actions from different viewpoints remains an extremely challenging problem, due to depth ambiguities, occlusion, and a large variety of appearances and scenes. Moreover, using only the information from the 2D perspective gives different interpretations for the same action seen from different viewpoints. Our system operates in two subsequent stages. The first stage estimates the 2D human pose using a convolution neural network. In the next stage, the 2D human poses are lifted to 3D human poses, using a temporal convolution neural network that enforces the temporal coherence over the estimated 3D poses. The estimated 3D poses from different viewpoints are then aligned to the same camera reference frame. Finally, we propose to use a temporal convolution network-based classifier for cross-view action recognition. Our results show that we can achieve state of art view-invariant action recognition accuracy even for the challenging viewpoints by only using RGB videos, without pre-training on synthetic or motion capture data. [less ▲]

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See detailBODYFITR: Robust Automatic 3D Human Body Fitting
Saint, Alexandre Fabian A UL; Shabayek, Abd El Rahman UL; Cherenkova, Kseniya UL et al

in Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP) (2019, September 22)

This paper proposes BODYFITR, a fully automatic method to fit a human body model to static 3D scans with complex poses. Automatic and reliable 3D human body fitting is necessary for many applications ... [more ▼]

This paper proposes BODYFITR, a fully automatic method to fit a human body model to static 3D scans with complex poses. Automatic and reliable 3D human body fitting is necessary for many applications related to healthcare, digital ergonomics, avatar creation and security, especially in industrial contexts for large-scale product design. Existing works either make prior assumptions on the pose, require manual annotation of the data or have difficulty handling complex poses. This work addresses these limitations by providing a novel automatic fitting pipeline with carefully integrated building blocks designed for a systematic and robust approach. It is validated on the 3DBodyTex dataset, with hundreds of high-quality 3D body scans, and shown to outperform prior works in static body pose and shape estimation, qualitatively and quantitatively. The method is also applied to the creation of realistic 3D avatars from the high-quality texture scans of 3DBodyTex, further demonstrating its capabilities. [less ▲]

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See detailVIEW-INVARIANT ACTION RECOGNITION FROM RGB DATA VIA 3D POSE ESTIMATION
Baptista, Renato UL; Ghorbel, Enjie UL; Papadopoulos, Konstantinos UL et al

in IEEE International Conference on Acoustics, Speech and Signal Processing, Brighton, UK, 12–17 May 2019 (2019, May)

In this paper, we propose a novel view-invariant action recognition method using a single monocular RGB camera. View-invariance remains a very challenging topic in 2D action recognition due to the lack of ... [more ▼]

In this paper, we propose a novel view-invariant action recognition method using a single monocular RGB camera. View-invariance remains a very challenging topic in 2D action recognition due to the lack of 3D information in RGB images. Most successful approaches make use of the concept of knowledge transfer by projecting 3D synthetic data to multiple viewpoints. Instead of relying on knowledge transfer, we propose to augment the RGB data by a third dimension by means of 3D skeleton estimation from 2D images using a CNN-based pose estimator. In order to ensure view-invariance, a pre-processing for alignment is applied followed by data expansion as a way for denoising. Finally, a Long-Short Term Memory (LSTM) architecture is used to model the temporal dependency between skeletons. The proposed network is trained to directly recognize actions from aligned 3D skeletons. The experiments performed on the challenging Northwestern-UCLA dataset show the superiority of our approach as compared to state-of-the-art ones. [less ▲]

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See detailA View-invariant Framework for Fast Skeleton-based Action Recognition Using a Single RGB Camera
Ghorbel, Enjie UL; Papadopoulos, Konstantinos UL; Baptista, Renato UL et al

in 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Prague, 25-27 February 2018 (2019, February)

View-invariant action recognition using a single RGB camera represents a very challenging topic due to the lack of 3D information in RGB images. Lately, the recent advances in deep learning made it ... [more ▼]

View-invariant action recognition using a single RGB camera represents a very challenging topic due to the lack of 3D information in RGB images. Lately, the recent advances in deep learning made it possible to extract a 3D skeleton from a single RGB image. Taking advantage of this impressive progress, we propose a simple framework for fast and view-invariant action recognition using a single RGB camera. The proposed pipeline can be seen as the association of two key steps. The first step is the estimation of a 3D skeleton from a single RGB image using a CNN-based pose estimator such as VNect. The second one aims at computing view-invariant skeleton-based features based on the estimated 3D skeletons. Experiments are conducted on two well-known benchmarks, namely, IXMAS and Northwestern-UCLA datasets. The obtained results prove the validity of our concept, which suggests a new way to address the challenge of RGB-based view-invariant action recognition. [less ▲]

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See detailTwo-stage RGB-based Action Detection using Augmented 3D Poses
Papadopoulos, Konstantinos UL; Ghorbel, Enjie UL; Baptista, Renato UL et al

in 18th International Conference on Computer Analysis of Images and Patterns SALERNO, 3-5 SEPTEMBER, 2019 (2019)

In this paper, a novel approach for action detection from RGB sequences is proposed. This concept takes advantage of the recent development of CNNs to estimate 3D human poses from a monocular camera. To ... [more ▼]

In this paper, a novel approach for action detection from RGB sequences is proposed. This concept takes advantage of the recent development of CNNs to estimate 3D human poses from a monocular camera. To show the validity of our method, we propose a 3D skeleton-based two-stage action detection approach. For localizing actions in unsegmented sequences, Relative Joint Position (RJP) and Histogram Of Displacements (HOD) are used as inputs to a k-nearest neighbor binary classifier in order to define action segments. Afterwards, to recognize the localized action proposals, a compact Long Short-Term Memory (LSTM) network with a de-noising expansion unit is employed. Compared to previous RGB-based methods, our approach offers robustness to radial motion, view-invariance and low computational complexity. Results on the Online Action Detection dataset show that our method outperforms earlier RGB-based approaches. [less ▲]

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See detailLocalized Trajectories for 2D and 3D Action Recognition
Papadopoulos, Konstantinos UL; Demisse, Girum UL; Ghorbel, Enjie UL et al

in Sensors (2019)

The Dense Trajectories concept is one of the most successful approaches in action recognition, suitable for scenarios involving a significant amount of motion. However, due to noise and background motion ... [more ▼]

The Dense Trajectories concept is one of the most successful approaches in action recognition, suitable for scenarios involving a significant amount of motion. However, due to noise and background motion, many generated trajectories are irrelevant to the actual human activity and can potentially lead to performance degradation. In this paper, we propose Localized Trajectories as an improved version of Dense Trajectories where motion trajectories are clustered around human body joints provided by RGB-D cameras and then encoded by local Bag-of-Words. As a result, the Localized Trajectories concept provides an advanced discriminative representation of actions. Moreover, we generalize Localized Trajectories to 3D by using the depth modality. One of the main advantages of 3D Localized Trajectories is that they describe radial displacements that are perpendicular to the image plane. Extensive experiments and analysis were carried out on five different datasets. [less ▲]

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See detailHome Self-Training: Visual Feedback for Assisting Physical Activity for Stroke Survivors
Baptista, Renato UL; Ghorbel, Enjie UL; Shabayek, Abd El Rahman UL et al

in Computer Methods and Programs in Biomedicine (2019)

Background and Objective: With the increase in the number of stroke survivors, there is an urgent need for designing appropriate home-based rehabilitation tools to reduce health-care costs. The objective ... [more ▼]

Background and Objective: With the increase in the number of stroke survivors, there is an urgent need for designing appropriate home-based rehabilitation tools to reduce health-care costs. The objective is to empower the rehabilitation of post-stroke patients at the comfort of their homes by supporting them while exercising without the physical presence of the therapist. Methods: A novel low-cost home-based training system is introduced. This system is designed as a composition of two linked applications: one for the therapist and another one for the patient. The therapist prescribes personalized exercises remotely, monitors the home-based training and re-adapts the exercises if required. On the other side, the patient loads the prescribed exercises, trains the prescribed exercise while being guided by color-based visual feedback and gets updates about the exercise performance. To achieve that, our system provides three main functionalities, namely: 1) Feedback proposals guiding a personalized exercise session, 2) Posture monitoring optimizing the effectiveness of the session, 3) Assessment of the quality of the motion. Results: The proposed system is evaluated on 10 healthy participants without any previous contact with the system. To analyze the impact of the feedback proposals, we carried out two different experimental sessions: without and with feedback proposals. The obtained results give preliminary assessments about the interest of using such feedback. Conclusions: Obtained results on 10 healthy participants are promising. This encourages to test the system in a realistic clinical context for the rehabilitation of stroke survivors. [less ▲]

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See detailDeformation-Based Abnormal Motion Detection using 3D Skeletons
Baptista, Renato UL; Demisse, Girum UL; Aouada, Djamila UL et al

in IEEE International Conference on Image Processing Theory, Tools and Applications (IPTA) (2018, November)

In this paper, we propose a system for abnormal motion detection using 3D skeleton information, where the abnormal motion is not known a priori. To that end, we present a curve-based representation of a ... [more ▼]

In this paper, we propose a system for abnormal motion detection using 3D skeleton information, where the abnormal motion is not known a priori. To that end, we present a curve-based representation of a sequence, based on few joints of a 3D skeleton, and a deformation-based distance function. We further introduce a time-variation model that is specifically designed for assessing the quality of a motion; we refer to a distance function that is based on such a model as~\emph{motion quality distance}. The overall advantages of the proposed approach are 1) lower dimensional yet representative sequence representation and 2) a distance function that emphasizes time variation, the motion quality distance, which is a particularly important property for quality assessment. We validate our approach using a publicly available dataset, SPHERE-StairCase2014 dataset. Qualitative and quantitative results show promising performance. [less ▲]

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See detailHighway Network Block with Gates Constraints for Training Very Deep Networks
Oyedotun, Oyebade UL; Shabayek, Abd El Rahman UL; Aouada, Djamila UL et al

in 2018 IEEE International Conference on Computer Vision and Pattern Recognition Workshop, June 18-22, 2018 (2018, June 19)

In this paper, we propose to reformulate the learning of the highway network block to realize both early optimization and improved generalization of very deep networks while preserving the network depth ... [more ▼]

In this paper, we propose to reformulate the learning of the highway network block to realize both early optimization and improved generalization of very deep networks while preserving the network depth. Gate constraints are duly employed to improve optimization, latent representations and parameterization usage in order to efficiently learn hierarchical feature transformations which are crucial for the success of any deep network. One of the earliest very deep models with over 30 layers that was successfully trained relied on highway network blocks. Although, highway blocks suffice for alleviating optimization problem via improved information flow, we show for the first time that further in training such highway blocks may result into learning mostly untransformed features and therefore a reduction in the effective depth of the model; this could negatively impact model generalization performance. Using the proposed approach, 15-layer and 20-layer models are successfully trained with one gate and a 32-layer model using three gates. This leads to a drastic reduction of model parameters as compared to the original highway network. Extensive experiments on CIFAR-10, CIFAR-100, Fashion-MNIST and USPS datasets are performed to validate the effectiveness of the proposed approach. Particularly, we outperform the original highway network and many state-ofthe- art results. To the best our knowledge, on the Fashion-MNIST and USPS datasets, the achieved results are the best reported in literature. [less ▲]

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See detailPose Encoding for Robust Skeleton-Based Action Recognition
Demisse, Girum UL; Papadopoulos, Konstantinos UL; Aouada, Djamila UL et al

in CVPRW: Visual Understanding of Humans in Crowd Scene, Salt Lake City, Utah, June 18-22, 2018 (2018, June 18)

Some of the main challenges in skeleton-based action recognition systems are redundant and noisy pose transformations. Earlier works in skeleton-based action recognition explored different approaches for ... [more ▼]

Some of the main challenges in skeleton-based action recognition systems are redundant and noisy pose transformations. Earlier works in skeleton-based action recognition explored different approaches for filtering linear noise transformations, but neglect to address potential nonlinear transformations. In this paper, we present an unsupervised learning approach for estimating nonlinear noise transformations in pose estimates. Our approach starts by decoupling linear and nonlinear noise transformations. While the linear transformations are modelled explicitly the nonlinear transformations are learned from data. Subsequently, we use an autoencoder with L2-norm reconstruction error and show that it indeed does capture nonlinear noise transformations, and recover a denoised pose estimate which in turn improves performance significantly. We validate our approach on a publicly available dataset, NW-UCLA. [less ▲]

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See detailKey-Skeleton Based Feedback Tool for Assisting Physical Activity
Baptista, Renato UL; Ghorbel, Enjie UL; Shabayek, Abd El Rahman UL et al

in 2018 Zooming Innovation in Consumer Electronics International Conference (ZINC), 30-31 May 2018 (2018, May 31)

This paper presents an intuitive feedback tool able to implicitly guide motion with respect to a reference movement. Such a tool is important in multiple applications requiring assisting physical ... [more ▼]

This paper presents an intuitive feedback tool able to implicitly guide motion with respect to a reference movement. Such a tool is important in multiple applications requiring assisting physical activities as in sports or rehabilitation. Our proposed approach is based on detecting key skeleton frames from a reference sequence of skeletons. The feedback is based on the 3D geometry analysis of the skeletons by taking into account the key-skeletons. Finally, the feedback is illustrated by a color-coded tool, which reflects the motion accuracy. [less ▲]

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