Exploiting deep residual networks for human action recognition from skeletal data

The computer vision community is currently focusing on solving action recognition problems in real videos, which contain thousands of samples with many challenges. In this process, Deep Convolutional Neural Networks (D-CNNs) have played a significant role in advancing the state-of-the-art in various vision-based action recognition systems. Recently, the introduction of residual connections in conjunction with a more traditional CNN model in a single architecture called Residual Network (ResNet) has shown impressive performance and great potential for image recognition tasks. In this paper, we investigate and apply deep ResNets for human action recognition using skeletal data provided by depth sensors. Firstly, the 3D coordinates of the human body joints carried in skeleton sequences are transformed into image-based representations and stored as RGB images. These color images are able to capture the spatial-temporal evolutions of 3D motions from skeleton sequences and can be efficiently learned by D-CNNs. We then propose a novel deep learning architecture based on ResNets to learn features from obtained color-based representations and classify them into action classes. The proposed method is evaluated on three challenging benchmark datasets including MSR Action 3D, KARD, and NTU-RGB+D datasets. Experimental results demonstrate that our method achieves state-of-the-art performance for all these benchmarks whilst requiring less computation resource. In particular, the proposed method surpasses previous approaches by a significant margin of 3.4% on MSR Action 3D dataset, 0.67% on KARD dataset, and 2.5% on NTU-RGB+D dataset

Unsupervised 3D Pose Estimation with Geometric Self-Supervision

We present an unsupervised learning approach to recover 3D human pose from 2D skeletal joints extracted from a single image. Our method does not require any multi-view image data, 3D skeletons, correspondences between 2D-3D points, or use previously learned 3D priors during training. A lifting network accepts 2D landmarks as inputs and generates a corresponding 3D skeleton estimate. During training, the recovered 3D skeleton is reprojected on random camera viewpoints to generate new "synthetic" 2D poses. By lifting the synthetic 2D poses back to 3D and re-projecting them in the original camera view, we can define self-consistency loss both in 3D and in 2D. The training can thus be self supervised by exploiting the geometric self-consistency of the lift-reproject-lift process. We show that self-consistency alone is not sufficient to generate realistic skeletons, however adding a 2D pose discriminator enables the lifter to output valid 3D poses. Additionally, to learn from 2D poses "in the wild", we train an unsupervised 2D domain adapter network to allow for an expansion of 2D data. This improves results and demonstrates the usefulness of 2D pose data for unsupervised 3D lifting. Results on Human3.6M dataset for 3D human pose estimation demonstrate that our approach improves upon the previous unsupervised methods by 30% and outperforms many weakly supervised approaches that explicitly use 3D data

Learning to Recognize 3D Human Action from A New Skeleton-based Representation Using Deep Convolutional Neural Networks

Recognizing human actions in untrimmed videos is an important challenging task. An effective 3D motion representation and a powerful learning model are two key factors influencing recognition performance. In this paper we introduce a new skeletonbased representation for 3D action recognition in videos. The key idea of the proposed representation is to transform 3D joint coordinates of the human body carried in skeleton sequences into RGB images via a color encoding process. By normalizing the 3D joint coordinates and dividing each skeleton frame into five parts, where the joints are concatenated according to the order of their physical connections, the color-coded representation is able to represent spatio-temporal evolutions of complex 3D motions, independently of the length of each sequence. We then design and train different Deep Convolutional Neural Networks (D-CNNs) based on the Residual Network architecture (ResNet) on the obtained image-based representations to learn 3D motion features and classify them into classes. Our method is evaluated on two widely used action recognition benchmarks: MSR Action3D and NTU-RGB+D, a very large-scale dataset for 3D human action recognition. The experimental results demonstrate that the proposed method outperforms previous state-of-the-art approaches whilst requiring less computation for training and prediction.This research was carried out at the Cerema Research Center (CEREMA) and Toulouse Institute of Computer Science Research (IRIT), Toulouse, France. Sergio A. Velastin is grateful for funding received from the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for Research, Technological Development and demonstration under grant agreement N. 600371, el Ministerio de Economia, Industria y Competitividad (COFUND2013-51509) el Ministerio de Educación, cultura y Deporte (CEI-15-17) and Banco Santander

Learning to recognise 3D human action from a new skeleton-based representation using deep convolutional neural networks

Recognising human actions in untrimmed videos is an important challenging task. An effective three-dimensional (3D) motion representation and a powerful learning model are two key factors influencing recognition performance. In this study, the authors introduce a new skeleton-based representation for 3D action recognition in videos. The key idea of the proposed representation is to transform 3D joint coordinates of the human body carried in skeleton sequences into RGB images via a colour encoding process. By normalising the 3D joint coordinates and dividing each skeleton frame into five parts, where the joints are concatenated according to the order of their physical connections, the colour-coded representation is able to represent spatio-temporal evolutions of complex 3D motions, independently of the length of each sequence. They then design and train different deep convolutional neural networks based on the residual network architecture on the obtained image-based representations to learn 3D motion features and classify them into classes. Their proposed method is evaluated on two widely used action recognition benchmarks: MSR Action3D and NTU-RGB+D, a very large-scale dataset for 3D human action recognition. The experimental results demonstrate that the proposed method outperforms previous state-of-the-art approaches while requiring less computation for training and prediction

Architectures d'apprentissage profond pour la reconnaissance d'actions humaines dans des séquences vidéo RGB-D monoculaires. Application à la surveillance dans les transports publics

Cette thèse porte sur la reconnaissance d'actions humaines dans des séquences vidéo RGB-D monoculaires. La question principale est, à partir d'une vidéo ou d'une séquence d'images donnée, de savoir comment reconnaître des actions particulières qui se produisent. Cette tâche est importante et est un défi majeur à cause d'un certain nombre de verrous scientifiques induits par la variabilité des conditions d'acquisition, comme l'éclairage, la position, l'orientation et le champ de vue de la caméra, ainsi que par la variabilité de la réalisation des actions, notamment de leur vitesse d'exécution. Pour surmonter certaines de ces difficultés, dans un premier temps, nous examinons et évaluons les techniques les plus récentes pour la reconnaissance d'actions dans des vidéos. Nous proposons ensuite une nouvelle approche basée sur des réseaux de neurones profonds pour la reconnaissance d'actions humaines à partir de séquences de squelettes 3D. Deux questions clés ont été traitées. Tout d'abord, comment représenter la dynamique spatio-temporelle d'une séquence de squelettes pour exploiter efficacement la capacité d'apprentissage des représentations de haut niveau des réseaux de neurones convolutifs (CNNs ou ConvNets). Ensuite, comment concevoir une architecture de CNN capable d'apprendre des caractéristiques spatio-temporelles discriminantes à partir de la représentation proposée dans un objectif de classification. Pour cela, nous introduisons deux nouvelles représentations du mouvement 3D basées sur des squelettes, appelées SPMF (Skeleton Posture-Motion Feature) et Enhanced-SPMF, qui encodent les postures et les mouvements humains extraits des séquences de squelettes sous la forme d'images couleur RGB. Pour les tâches d'apprentissage et de classification, nous proposons différentes architectures de CNNs, qui sont basées sur les modèles Residual Network (ResNet), Inception-ResNet-v2, Densely Connected Convolutional Network (DenseNet) et Efficient Neural Architecture Search (ENAS), pour extraire des caractéristiques robustes de la représentation sous forme d'image que nous proposons et pour les classer. Les résultats expérimentaux sur des bases de données publiques (MSR Action3D, Kinect Activity Recognition Dataset, SBU Kinect Interaction, et NTU-RGB+D) montrent que notre approche surpasse les méthodes de l'état de l'art. Nous proposons également une nouvelle technique pour l'estimation de postures humaines à partir d'une vidéo RGB. Pour cela, le modèle d'apprentissage profond appelé OpenPose est utilisé pour détecter les personnes et extraire leur posture en 2D. Un réseau de neurones profond est ensuite proposé pour apprendre la transformation permettant de reconstruire ces postures en trois dimensions. Les résultats expérimentaux sur la base de données Human3.6M montrent l'efficacité de la méthode proposée. Ces résultats ouvrent des perspectives pour une approche de la reconnaissance d'actions humaines à partir des séquences de squelettes 3D sans utiliser des capteurs de profondeur comme la Kinect. Nous avons également constitué la base CEMEST, une nouvelle base de données RGB-D illustrant des comportements de passagers dans les transports publics. Elle contient 203 vidéos de surveillance collectées dans une station du métro incluant des événements "normaux" et "anormaux". Nous avons obtenu des résultats prometteurs sur cette base en utilisant des techniques d'augmentation de données et de transfert d'apprentissage. Notre approche permet de concevoir des applications basées sur des techniques de l'apprentissage profond pour renforcer la qualité des services de transport en commun.This thesis is dealing with automatic recognition of human actions from monocular RGB-D video sequences. Our main goal is to recognize which human actions occur in unknown videos. This problem is a challenging task due to a number of obstacles caused by the variability of the acquisition conditions, including the lighting, the position, the orientation and the field of view of the camera, as well as the variability of actions which can be performed differently, notably in terms of speed. To tackle these problems, we first review and evaluate the most prominent state-of-the-art techniques to identify the current state of human action recognition in videos. We then propose a new approach for skeleton-based action recognition using Deep Neural Networks (DNNs). Two key questions have been addressed. First, how to efficiently represent the spatio-temporal patterns of skeletal data for fully exploiting the capacity in learning high-level representations of Deep Convolutional Neural Networks (D-CNNs). Second, how to design a powerful D-CNN architecture that is able to learn discriminative features from the proposed representation for classification task. As a result, we introduce two new 3D motion representations called SPMF (Skeleton Posture-Motion Feature) and Enhanced-SPMF that encode skeleton poses and their motions into color images. For learning and classification tasks, we design and train different D-CNN architectures based on the Residual Network (ResNet), Inception-ResNet-v2, Densely Connected Convolutional Network (DenseNet) and Efficient Neural Architecture Search (ENAS) to extract robust features from color-coded images and classify them. Experimental results on various public and challenging human action recognition datasets (MSR Action3D, Kinect Activity Recognition Dataset, SBU Kinect Interaction, and NTU-RGB+D) show that the proposed approach outperforms current state-of-the-art. We also conducted research on the problem of 3D human pose estimation from monocular RGB video sequences and exploited the estimated 3D poses for recognition task. Specifically, a deep learning-based model called OpenPose is deployed to detect 2D human poses. A DNN is then proposed and trained for learning a 2D-to-3D mapping in order to map the detected 2D keypoints into 3D poses. Our experiments on the Human3.6M dataset verified the effectiveness of the proposed method. These obtained results allow opening a new research direction for human action recognition from 3D skeletal data, when the depth cameras are failing. In addition, we collect and introduce in this thesis, CEMEST database, a new RGB-D dataset depicting passengers' behaviors in public transport. It consists of 203 untrimmed real-world surveillance videos of realistic "normal" and "abnormal" events. We achieve promising results on CEMEST with the support of data augmentation and transfer learning techniques. This enables the construction of real-world applications based on deep learning for enhancing public transportation management services

An original framework for understanding human actions and body language by using deep neural networks

The evolution of both fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has allowed the development of efficient automatic systems for the analysis of people's behaviour. By studying hand movements it is possible to recognize gestures, often used by people to communicate information in a non-verbal way. These gestures can also be used to control or interact with devices without physically touching them. In particular, sign language and semaphoric hand gestures are the two foremost areas of interest due to their importance in Human-Human Communication (HHC) and Human-Computer Interaction (HCI), respectively. While the processing of body movements play a key role in the action recognition and affective computing fields. The former is essential to understand how people act in an environment, while the latter tries to interpret people's emotions based on their poses and movements; both are essential tasks in many computer vision applications, including event recognition, and video surveillance. In this Ph.D. thesis, an original framework for understanding Actions and body language is presented. The framework is composed of three main modules: in the first one, a Long Short Term Memory Recurrent Neural Networks (LSTM-RNNs) based method for the Recognition of Sign Language and Semaphoric Hand Gestures is proposed; the second module presents a solution based on 2D skeleton and two-branch stacked LSTM-RNNs for action recognition in video sequences; finally, in the last module, a solution for basic non-acted emotion recognition by using 3D skeleton and Deep Neural Networks (DNNs) is provided. The performances of RNN-LSTMs are explored in depth, due to their ability to model the long term contextual information of temporal sequences, making them suitable for analysing body movements. All the modules were tested by using challenging datasets, well known in the state of the art, showing remarkable results compared to the current literature methods