Multi-spectral and multi-perspective video arrays for driver body tracking and activity analysis

Authors:
Shinko Y. Cheng;Sangho Park;Mohan M. Trivedi
Affiliations:
University of California, San Diego, Department of Electrical and Computer Engineering, 9500 Gilman Drive MC 0434, La Jolla, CA 92093-0434, USA;University of California, San Diego, Department of Electrical and Computer Engineering, 9500 Gilman Drive MC 0434, La Jolla, CA 92093-0434, USA;University of California, San Diego, Department of Electrical and Computer Engineering, 9500 Gilman Drive MC 0434, La Jolla, CA 92093-0434, USA
Venue:
Computer Vision and Image Understanding
Year:
2007

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Abstract

This paper presents a novel approach to recognizing driver activities using a multi-perspective (i.e., four camera views) multi-modal (i.e., thermal infrared and color) video-based system for robust and real-time tracking of important body parts. The multi-perspective characteristics of the system provides redundant trajectories of the body parts, while the multi-modal characteristics of the system provides robustness and reliability of feature detection and tracking. The combination of a deterministic activity grammar (called 'operation-triplet') and a Hidden Markov model-based classifier provides semantic-level analysis of human activity. The application context for this research is that of intelligent vehicles and driver assistance systems. Experimental results in real-world street driving demonstrate effectiveness of the proposed system.