Automatic surveillance analyzer using trajectory and body-based modeling

With the continuous improvements in computer-vision techniques, automatic low-cost video surveillance gradually emerges for consumer applications. Successful trajectory estimation and human-body modeling facilitate the semantic analysis of human activities in video sequences. We propose a fast analyzer for surveillance video, which aims at automatic analysis of human behavior and semantic events. Our analyzer employs visual cues to track moving persons and classify human-body postures from a monocular video. It consists of three processing steps: (1) multi-person detection, (2) persons tracking with trajectory estimation, and (3) posture classification. We show attractive experimental results, highlighting the system efficiency and classification capability

Human motion analysis using simultaneous trajectory and body detection and modeling

In this paper, we propose a scheme to combine trajectory-based detection and body-based estimation to analyze human behavior in video scene. Our scheme is applied for a fast and automatic detection of pick-up/drop-off events within surveillance videos in indoor areas. A moving person is tracked globally using the mean-shift algorithm and modeled locally using an axis skeleton in a monocular video sequence. We detect and rectify the stationary pick-up/drop-off objects. The spatial-temporal relationship between object and person is measured and exploited to detect a pick-up/drop-off event. Our experimental results accu rately estimate the human-motion trajectory and infer the posture. The system operates at real-time speed (around 20 frames/second)

A matching-based approach for human motion analysis

This paper presents a novel approach to implement estimation and recognition of human motion from uncalibrated monocular video sequences. As it is difficult to find a good motion description for humans, we propose a matching scheme based on a local descriptor and a global descriptor, to detect individual body parts and analyze the shape of the whole body as well. In a frame-by-frame process, both descriptors are combined to implement the matching of the motion pattern and the body orientation. Moreover, we have added a novel spatial-temporal cost factor in the matching scheme which aims at increasing the temporal consistency and reliability of the description. We tested the algorithms on the CMU MoBo database with promising results. The method achieves the motion-type recognition and body-orientation classification at the accuracy of 95% and 98%, respectively. The system can be utilized for an effective human-motion analysis from a monocular video