Time-slice analysis of dyadic human activity

La reconnaissance d’activités humaines à partir de données vidéo est utilisée pour la surveillance ainsi que pour des applications d’interaction homme-machine. Le principal objectif est de classer les vidéos dans l’une des k classes d’actions à partir de vidéos entièrement observées. Cependant, de tout temps, les systèmes intelligents sont améliorés afin de prendre des décisions basées sur des incertitudes et ou des informations incomplètes. Ce besoin nous motive à introduire le problème de l’analyse de l’incertitude associée aux activités humaines et de pouvoir passer à un nouveau niveau de généralité lié aux problèmes d’analyse d’actions. Nous allons également présenter le problème de reconnaissance d’activités par intervalle de temps, qui vise à explorer l’activité humaine dans un intervalle de temps court. Il a été démontré que l’analyse par intervalle de temps est utile pour la caractérisation des mouvements et en général pour l’analyse de contenus vidéo. Ces études nous encouragent à utiliser ces intervalles de temps afin d’analyser l’incertitude associée aux activités humaines. Nous allons détailler à quel degré de certitude chaque activité se produit au cours de la vidéo. Dans cette thèse, l’analyse par intervalle de temps d’activités humaines avec incertitudes sera structurée en 3 parties. i) Nous présentons une nouvelle famille de descripteurs spatiotemporels optimisés pour la prédiction précoce avec annotations d’intervalle de temps. Notre représentation prédictive du point d’intérêt spatiotemporel (Predict-STIP) est basée sur l’idée de la contingence entre intervalles de temps. ii) Nous exploitons des techniques de pointe pour extraire des points d’intérêts afin de représenter ces intervalles de temps. iii) Nous utilisons des relations (uniformes et par paires) basées sur les réseaux neuronaux convolutionnels entre les différentes parties du corps de l’individu dans chaque intervalle de temps. Les relations uniformes enregistrent l’apparence locale de la partie du corps tandis que les relations par paires captent les relations contextuelles locales entre les parties du corps. Nous extrayons les spécificités de chaque image dans l’intervalle de temps et examinons différentes façons de les agréger temporellement afin de générer un descripteur pour tout l’intervalle de temps. En outre, nous créons une nouvelle base de données qui est annotée à de multiples intervalles de temps courts, permettant la modélisation de l’incertitude inhérente à la reconnaissance d’activités par intervalle de temps. Les résultats expérimentaux montrent l’efficience de notre stratégie dans l’analyse des mouvements humains avec incertitude.Recognizing human activities from video data is routinely leveraged for surveillance and human-computer interaction applications. The main focus has been classifying videos into one of k action classes from fully observed videos. However, intelligent systems must to make decisions under uncertainty, and based on incomplete information. This need motivates us to introduce the problem of analysing the uncertainty associated with human activities and move to a new level of generality in the action analysis problem. We also present the problem of time-slice activity recognition which aims to explore human activity at a small temporal granularity. Time-slice recognition is able to infer human behaviours from a short temporal window. It has been shown that temporal slice analysis is helpful for motion characterization and for video content representation in general. These studies motivate us to consider timeslices for analysing the uncertainty associated with human activities. We report to what degree of certainty each activity is occurring throughout the video from definitely not occurring to definitely occurring. In this research, we propose three frameworks for time-slice analysis of dyadic human activity under uncertainty. i) We present a new family of spatio-temporal descriptors which are optimized for early prediction with time-slice action annotations. Our predictive spatiotemporal interest point (Predict-STIP) representation is based on the intuition of temporal contingency between time-slices. ii) we exploit state-of-the art techniques to extract interest points in order to represent time-slices. We also present an accumulative uncertainty to depict the uncertainty associated with partially observed videos for the task of early activity recognition. iii) we use Convolutional Neural Networks-based unary and pairwise relations between human body joints in each time-slice. The unary term captures the local appearance of the joints while the pairwise term captures the local contextual relations between the parts. We extract these features from each frame in a time-slice and examine different temporal aggregations to generate a descriptor for the whole time-slice. Furthermore, we create a novel dataset which is annotated at multiple short temporal windows, allowing the modelling of the inherent uncertainty in time-slice activity recognition. All the three methods have been evaluated on TAP dataset. Experimental results demonstrate the effectiveness of our framework in the analysis of dyadic activities under uncertaint

Human Pose Estimation from Monocular Images : a Comprehensive Survey

Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problema into several modules: feature extraction and description, human body models, and modelin methods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used

Multigranularity Representations for Human Inter-Actions: Pose, Motion and Intention

Tracking people and their body pose in videos is a central problem in computer vision. Standard tracking representations reason about temporal coherence of detected people and body parts. They have difficulty tracking targets under partial occlusions or rare body poses, where detectors often fail, since the number of training examples is often too small to deal with the exponential variability of such configurations. We propose tracking representations that track and segment people and their body pose in videos by exploiting information at multiple detection and segmentation granularities when available, whole body, parts or point trajectories. Detections and motion estimates provide contradictory information in case of false alarm detections or leaking motion affinities. We consolidate contradictory information via graph steering, an algorithm for simultaneous detection and co-clustering in a two-granularity graph of motion trajectories and detections, that corrects motion leakage between correctly detected objects, while being robust to false alarms or spatially inaccurate detections. We first present a motion segmentation framework that exploits long range motion of point trajectories and large spatial support of image regions. We show resulting video segments adapt to targets under partial occlusions and deformations. Second, we augment motion-based representations with object detection for dealing with motion leakage. We demonstrate how to combine dense optical flow trajectory affinities with repulsions from confident detections to reach a global consensus of detection and tracking in crowded scenes. Third, we study human motion and pose estimation. We segment hard to detect, fast moving body limbs from their surrounding clutter and match them against pose exemplars to detect body pose under fast motion. We employ on-the-fly human body kinematics to improve tracking of body joints under wide deformations. We use motion segmentability of body parts for re-ranking a set of body joint candidate trajectories and jointly infer multi-frame body pose and video segmentation. We show empirically that such multi-granularity tracking representation is worthwhile, obtaining significantly more accurate multi-object tracking and detailed body pose estimation in popular datasets

Learning activity progression in LSTMs for activity detection and early detection

In this work we improve training of temporal deep models to better learn activity progression for activity detection and early detection tasks. Conventionally, when training a Recurrent Neural Network, specifically a Long Short Term Memory (LSTM) model, the training loss only considers classification error. However, we argue that the detection score of the correct activity category, or the detection score margin between the correct and incorrect categories, should be monotonically non-decreasing as the model observes more of the activity. We design novel ranking losses that directly penalize the model on violation of such monotonicities, which are used together with classification loss in training of LSTM models. Evaluation on ActivityNet shows significant benefits of the proposed ranking losses in both activity detection and early detection tasks.https://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Ma_Learning_Activity_Progression_CVPR_2016_paper.htmlPublished versio