A generic framework for video understanding applied to group behavior recognition

This paper presents an approach to detect and track groups of people in video-surveillance applications, and to automatically recognize their behavior. This method keeps track of individuals moving together by maintaining a spacial and temporal group coherence. First, people are individually detected and tracked. Second, their trajectories are analyzed over a temporal window and clustered using the Mean-Shift algorithm. A coherence value describes how well a set of people can be described as a group. Furthermore, we propose a formal event description language. The group events recognition approach is successfully validated on 4 camera views from 3 datasets: an airport, a subway, a shopping center corridor and an entrance hall.Comment: (20/03/2012

Human Posture Recognition in Video Sequence

International audienceThis paper presents a new approach to recognize human postures in video sequences comparing two methods. We first describe these two methods based on 2D appearances. The first one uses projections of moving pixels on the reference axis. The second method decomposes the human silhouette into blocks and learns 2D posture appearances through PCA. Then we use 3D model of posture to make the previous methods independent of the camera position. At the end we give some preliminary results and conclude on the effectiveness of this approach

Applying 3D Human Model in a Posture Recognition System

International audienceThis paper proposes an approach to recognise human postures in videosequences, which combines a 2D approach with a 3D human model. The 3D model is a realistic articulated human model which is used to obtain reference postures to compare with test postures. Several 2D approaches using different silhouette representations are compared with each other: projections of moving pixels on the reference axis, Hu moments and skeletonisation. We are interested in a set of specific postures which are representative of typical video understanding applications. We describe results for recognition of general postures (e.g. standing) and detailed postures (e.g standing with one arm up) in ambiguous/optimal viewpoint with good/bad segmented silhouette to show the effectiveness of our approach

Posture Recognition with a 3D Human Model

International audienceThis paper proposes an approach to recognise human postures in video sequences, which combines a 2D approach with a 3D human model. The 2D approach consists in projections of moving pixels on the reference axis. The 3D model is a realistic articulated human model which is used to obtain reference postures to compare with test postures. We are interested in a set of specific postures which are representative of typical applications in video interpretation. We give results for recognition of general (e.g. standing) and detailed (e.g standing with one arm up) postures. First results show the effectiveness of our approach for recognition of human posture

Improving Person Re-identification by Viewpoint Cues

International audienceRe-identifying people in a network of cameras requires an invariant human representation. State of the art algorithms are likely to fail in real-world scenarios due to serious perspective changes. Most of existing approaches focus on invariant and discriminative features, while ignoring the body alignment issue. In this paper we propose 3 methods for improving the performance of person re-identification. We focus on eliminating perspective distortions by using 3D scene information. Perspective changes are minimized by affine transformations of cropped images containing the target (1). Further we estimate the human pose for (2) clustering data from a video stream and (3) weighting image features. The pose is estimated using 3D scene information and motion of the target. We validated our approach on a publicly available dataset with a network of 8 cameras. The results demonstrated significant increase in the re-identification performance over the state of the art

Monitoring Activities of Daily Living (ADLs) of Elderly Based on 3D Key Human Postures

International audienceThis paper presents a cognitive vision approach to recognize a set of interesting activities of daily living (ADLs) for elderly at home. The proposed approach is composed of a video analysis component and an activity recognition component. A video analysis component contains person detection, person tracking and human posture recognition. A human posture recognition is composed of a set of postures models and a dedicated human posture recognition algorithm. Activity recognition component contains a set of video event models and a dedicated video event recognition algorithm. In this study, we collaborate with medical experts (gerontologists from Nice hospital) to define and model a set of scenarios related to the interesting activities of elderly. In our approach, we propose ten 3D key human postures usefull to recognize a set of interesting human activities regardless of the environment. The novelty of our approach is the proposed 3D key postures and the set of activity models of elderly person living alone in her/his own home. To validate our proposed models, we have performed a set of experiments in the Gerhome laboratory which is a realistic site reproducing the environment of a typical apartment. For these experiments, we have acquired and processed ten video sequences with one actor. The duration of each video sequence is about ten minute

Group interaction and group tracking for video-surveillance in underground railway stations

International audienceIn this paper we propose an approach to recognize behaviors of groups of people in the subway. Violent behavior or vandalism performed by a group can be detected in order to alert subway security. The proposed system is composed of 3 main layers: the detection of people in the video, the detection and tracking of groups among the detected individuals and the detection of events and scenarios of interest based on tracked actors (groups). The main focus of this paper are the group tracking and event detection layers

Pulse-shape discrimination and energy resolution of a liquid-argon scintillator with xenon doping

Liquid-argon scintillation detectors are used in fundamental physics experiments and are being considered for security applications. Previous studies have suggested that the addition of small amounts of xenon dopant improves performance in light or signal yield, energy resolution, and particle discrimination. In this study, we investigate the detector response for xenon dopant concentrations from 9 +/- 5 ppm to 1100 +/- 500 ppm xenon (by weight) in 6 steps. The 3.14-liter detector uses tetraphenyl butadiene (TPB) wavelength shifter with dual photomultiplier tubes and is operated in single-phase mode. Gamma-ray-interaction signal yield of 4.0 +/- 0.1 photoelectrons/keV improved to 5.0 +/- 0.1 photoelectrons/keV with dopant. Energy resolution at 662 keV improved from (4.4 +/- 0.2)% ({\sigma}) to (3.5 +/- 0.2)% ({\sigma}) with dopant. Pulse-shape discrimination performance degraded greatly at the first addition of dopant, slightly improved with additional additions, then rapidly improved near the end of our dopant range, with performance becoming slightly better than pure argon at the highest tested dopant concentration. Some evidence of reduced neutron scintillation efficiency with increasing dopant concentration was observed. Finally, the waveform shape outside the TPB region is discussed, suggesting that the contribution to the waveform from xenon-produced light is primarily in the last portion of the slow component

Comparison of established and emerging biodosimetry assays

Rapid biodosimetry tools are required to assist with triage in the case of a large-scale radiation incident. Here, we aimed to determine the dose-assessment accuracy of the well-established dicentric chromosome assay (DCA) and cytokinesis-block micronucleus assay (CBMN) in comparison to the emerging γ-H2AX foci and gene expression assays for triage mode biodosimetry and radiation injury assessment. Coded blood samples exposed to 10 X-ray doses (240 kVp, 1 Gy/min) of up to 6.4 Gy were sent to participants for dose estimation. Report times were documented for each laboratory and assay. The mean absolute difference (MAD) of estimated doses relative to the true doses was calculated. We also merged doses into binary dose categories of clinical relevance and examined accuracy, sensitivity and specificity of the assays. Dose estimates were reported by the first laboratories within 0.3-0.4 days of receipt of samples for the γ-H2AX and gene expression assays compared to 2.4 and 4 days for the DCA and CBMN assays, respectively. Irrespective of the assay we found a 2.5-4-fold variation of interlaboratory accuracy per assay and lowest MAD values for the DCA assay (0.16 Gy) followed by CBMN (0.34 Gy), gene expression (0.34 Gy) and γ-H2AX (0.45 Gy) foci assay. Binary categories of dose estimates could be discriminated with equal efficiency for all assays, but at doses ≥1.5 Gy a 10% decrease in efficiency was observed for the foci assay, which was still comparable to the CBMN assay. In conclusion, the DCA has been confirmed as the gold standard biodosimetry method, but in situations where speed and throughput are more important than ultimate accuracy, the emerging rapid molecular assays have the potential to become useful triage tools