期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 10, 期 4, 页码 628-638出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2009.2026440
关键词
Context-free grammars; intelligent transportation system (ITS) applications; machine learning; vehicle tracking; video analysis
资金
- National Science Foundation [IIS-0219863, CNS-0224363, CNS-0324864, CNS-0420836, IIP-0443945, IIP-0726109, CNS-0708344]
- ITS Institute at the University of Minnesota
- Minnesota Department of Transportation
A key research issue in activity recognition in real-world applications, such as in intelligent transportation systems (ITS), is to automatically learn robust models of activities that require minimal human training. In this paper, we contribute a novel approach for learning sequenced spatiotemporal activities in outdoor traffic intersections. Concretely, by representing the activities as sequences of actions, we contribute a semisupervised learning algorithm that learns activities as complete stochastic context-free grammars (SCFGs), namely, the grammar structure and the parameters. Our approach has been implemented and tested on real-world scenes, and we present experimental results of the grammar learning and activity recognition applied to data collection and traffic monitoring applications using video data.
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