Multi-module human motion analysis from a monocular video

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.