Middle-level representation for human activities recognition : the role of spatio-temporal relationships

• Main Authors: Yuan, Fei, Prinet, V´eronique, Yuan, Junsong
• Other Authors: Kutulakos, Kiriakos N.
• Format: Book Chapter
• Language: English
• Published: Springer Berlin Heidelberg 2014
• Subjects: DRNTU::Engineering::Electrical and electronic engineering
• Online Access: https://hdl.handle.net/10356/103853; http://hdl.handle.net/10220/19350
• Institution: Nanyang Technological University

We tackle the challenging problem of human activity recognition in realistic video sequences. Unlike local features-based methods or global template-based methods, we propose to represent a video sequence by a set of middle-level parts. A part, or component, has consistent spatial structure and consistent motion. We first segment the visual motion patterns and generate a set of middle-level components by clustering keypoints-based trajectories extracted from the video. To further exploit the interdependencies of the moving parts, we then define spatio-temporal relationships between pairwise components. The resulting descriptive middle-level components and pairwise-components thereby catch the essential motion characteristics of human activities. They also give a very compact representation of the video. We apply our framework on popular and challenging video datasets: Weizmann dataset and UT-Interaction dataset. We demonstrate experimentally that our middle-level representation combined with a χ 2-SVM classifier equals to or outperforms the state-of-the-art results on these dataset.