Multi-level human motion analysis for surveillance applications

In this paper, we study a flexible framework for semantic analysis of human motion from a monocular surveillance video. Successful trajectory estimation and human-body modeling facilitate the semantic analysis of human activities in video sequences. As a first contribution, we propose a flexible framework that enables automatic analysis of human behavior and semantic events. It can be utilized in surveillance applications with four-level analysis results. The second contribution is the introduction of a 3-D reconstruction scheme for scene understanding. The total framework consists of four processing levels: (1) a pre-processing level including background modeling and multiple-person detection, (2) an object-based level performing trajectory estimation and posture classification, (3) an event-based level for semantic analysis and (4) a visualization level including camera calibration and 3-D scene reconstruction. Our proposed framework was evaluated and proved its effectiveness as it achieves a near real-time performance (6-8 frames/second)