Mixed-State Models for Nonstationary Multiobject Activities. (27th December 2006)

• Record Type: Journal Article
• Authors: Cuntoor Cuntoor, Naresh P. Naresh P.; Chellappa Chellappa, Rama Rama
• Other Names: De Natale De Natale Francesco G. B. Francesco G. B. Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2007(2007)
• Publication Date: 2006-12-27
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2007/65989
• Languages: English
• ISSNs: 1687-6172

Abstract

Abstract : We present a mixed-state space approach for modeling and segmenting human activities. The discrete-valued component of the mixed state represents higher-level behavior while the continuous state models the dynamics within behavioral segments. A basis of behaviors based on generic properties of motion trajectories is chosen to characterize segments of activities. A Viterbi-based algorithm to detect boundaries between segments is described. The usefulness of the proposed approach for temporal segmentation and anomaly detection is illustrated using the TSA airport tarmac surveillance dataset, the bank monitoring dataset, and the UCF database of human actions.