Citation

Veeraraghavan H, Papanikolopoulos NP. IEEE Trans. Intel. Transp. Syst. 2009; 10(4): 628-638.

Copyright

(Copyright © 2009, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2009.2026440

PMID

unavailable

Abstract

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.