Mixed-state models for nonstationary multiobject activities

TitleMixed-state models for nonstationary multiobject activities
Publication TypeJournal Articles
Year of Publication2007
AuthorsCuntoor NP, Chellappa R
JournalEURASIP J. Appl. Signal Process.
Pagination106 - 106
Date Published2007/01//
ISBN Number1110-8657

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.