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).
In this paper, we propose an effective framework for semantic analysis of human motion from a monocular
video. As it is difficult to find a good motion description for humans, we focus on a reliable recognition of the
motion type and estimate the body orientation involved in the video sequence. Our framework analyzes the
body motion in three modules: a pre-processing module, matching module and semantic module. The proposed
framework includes novel object-level processing algorithms, such as a local descriptor and a global descriptor
to detect body parts and analyze the shape of the whole body as well. Both descriptors jointly contribute to the
matching process by incorporating them into a new weighted linear combination for matching. We also introduce
a simple cost function based on time-index di.erences to distinguish motion types and cycles in human motions.
Our system can provide three different types of analysis results: (1) foreground person detection; (2) motion
recognition in the sequence; (3) 3-D modeling of human motion based on generic human models. The proposed
framework was evaluated and proved its effectiveness as it achieves the motion recognition and body-orientation
classification at the accuracy of 95% and 98%, respectively.
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