Calibration-free Pedestrian Partial Pose Estimation Using a High-mounted Kinect

Les applications de l’analyse du comportement humain ont subit de rapides développements durant les dernières décades, tant au niveau des systèmes de divertissements que pour des applications professionnelles comme les interfaces humain-machine, les systèmes d’assistance de conduite automobile ou des systèmes de protection des piétons. Cette thèse traite du problème de reconnaissance de piétons ainsi qu’à l’estimation de leur orientation en 3D. Cette estimation est faite dans l’optique que la connaissance de cette orientation est bénéfique tant au niveau de l’analyse que de la prédiction du comportement des piétons. De ce fait, cette thèse propose à la fois une nouvelle méthode pour détecter les piétons et une manière d’estimer leur orientation, par l’intégration séquentielle d’un module de détection et un module d’estimation d’orientation. Pour effectuer cette détection de piéton, nous avons conçu un classificateur en cascade qui génère automatiquement une boîte autour des piétons détectés dans l’image. Suivant cela, des régions sont extraites d’un nuage de points 3D afin de classifier l’orientation du torse du piéton. Cette classification se base sur une image synthétique grossière par tramage (rasterization) qui simule une caméra virtuelle placée immédiatement au-dessus du piéton détecté. Une machine à vecteurs de support effectue la classification à partir de cette image de synthèse, pour l’une des 10 orientations discrètes utilisées lors de l’entrainement (incréments de 30 degrés). Afin de valider les performances de notre approche d’estimation d’orientation, nous avons construit une base de données de référence contenant 764 nuages de points. Ces données furent capturées à l’aide d’une caméra Kinect de Microsoft pour 30 volontaires différents, et la vérité-terrain sur l’orientation fut établie par l’entremise d’un système de capture de mouvement Vicon. Finalement, nous avons démontré les améliorations apportées par notre approche. En particulier, nous pouvons détecter des piétons avec une précision de 95.29% et estimer l’orientation du corps (dans un intervalle de 30 degrés) avec une précision de 88.88%. Nous espérons ainsi que nos résultats de recherche puissent servir de point de départ à d’autres recherches futures.The application of human behavior analysis has undergone rapid development during the last decades from entertainment system to professional one, as Human Robot Interaction (HRI), Advanced Driver Assistance System (ADAS), Pedestrian Protection System (PPS), etc. Meanwhile, this thesis addresses the problem of recognizing pedestrians and estimating their body orientation in 3D based on the fact that estimating a person’s orientation is beneficial in determining their behavior. In this thesis, a new method is proposed for detecting and estimating the orientation, in which the result of a pedestrian detection module and a orientation estimation module are integrated sequentially. For the goal of pedestrian detection, a cascade classifier is designed to draw a bounding box around the detected pedestrian. Following this, extracted regions are given to a discrete orientation classifier to estimate pedestrian body’s orientation. This classification is based on a coarse, rasterized depth image simulating a top-view virtual camera, and uses a support vector machine classifier that was trained to distinguish 10 orientations (30 degrees increments). In order to test the performance of our approach, a new benchmark database contains 764 sets of point cloud for body-orientation classification was captured. For this benchmark, a Kinect recorded the point cloud of 30 participants and a marker-based motion capture system (Vicon) provided the ground truth on their orientation. Finally we demonstrated the improvements brought by our system, as it detected pedestrian with an accuracy of 95:29% and estimated the body orientation with an accuracy of 88:88%.We hope it can provide a new foundation for future researches

Carried baggage detection and recognition in video surveillance with foreground segmentation

Security cameras installed in public spaces or in private organizations continuously record video data with the aim of detecting and preventing crime. For that reason, video content analysis applications, either for real time (i.e. analytic) or post-event (i.e. forensic) analysis, have gained high interest in recent years. In this thesis, the primary focus is on two key aspects of video analysis, reliable moving object segmentation and carried object detection & identification. A novel moving object segmentation scheme by background subtraction is presented in this thesis. The scheme relies on background modelling which is based on multi-directional gradient and phase congruency. As a post processing step, the detected foreground contours are refined by classifying the edge segments as either belonging to the foreground or background. Further contour completion technique by anisotropic diffusion is first introduced in this area. The proposed method targets cast shadow removal, gradual illumination change invariance, and closed contour extraction. A state of the art carried object detection method is employed as a benchmark algorithm. This method includes silhouette analysis by comparing human temporal templates with unencumbered human models. The implementation aspects of the algorithm are improved by automatically estimating the viewing direction of the pedestrian and are extended by a carried luggage identification module. As the temporal template is a frequency template and the information that it provides is not sufficient, a colour temporal template is introduced. The standard steps followed by the state of the art algorithm are approached from a different extended (by colour information) perspective, resulting in more accurate carried object segmentation. The experiments conducted in this research show that the proposed closed foreground segmentation technique attains all the aforementioned goals. The incremental improvements applied to the state of the art carried object detection algorithm revealed the full potential of the scheme. The experiments demonstrate the ability of the proposed carried object detection algorithm to supersede the state of the art method

Contextual Person Identification in Multimedia Data

We propose methods to improve automatic person identification, regardless of the visibility of a face, by integration of multiple cues including multiple modalities and contextual information. We propose a joint learning approach using contextual information from videos to improve learned face models. Further, we integrate additional modalities in a global fusion framework. We evaluate our approaches on a novel TV series data set, consisting of over 100 000 annotated faces

Combined estimation of location and body pose in surveillance video

In surveillance videos, cues such as head or body pose provide important information for analyzing people’s behavior and interactions. In this paper we propose an approach that jointly estimates body location and body pose in monocular surveillance video. Our approach is based on tracks derived by multi-object tracking. First, body pose classification is conducted using sparse representation technique on each frame of the tracks, generating (noisy) observation on body poses. Then, both location and body pose in 3D space are estimated jointly in a particle filtering framework by utilizing a soft coupling of body pose with the movement. The experiments show that the proposed system successfully tracks body position and pose simultaneously in many scenarios. The output of the system can be used to perform further analysis on behaviors and interactions. 1