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.
Highlights
Modeling complex activities involves extracting spatiotemporal descriptors associated with objects moving in a scene
Many existing approaches assume that the structure of activities is known; and a fixed number of free parameters is determined based on experience or by estimating the model order
We demonstrate the usefulness of the online behavior-driven mixedstate (BMS) model for temporal segmentation and the offline BMS model for anomaly detection using the following three datasets: the TSA airport surveillance dataset, bank dataset, and the UCF human action dataset
Summary
Modeling complex activities involves extracting spatiotemporal descriptors associated with objects moving in a scene. There exists a hierarchical relationship extending from observed features to higher-level behaviors of moving objects Features such as motion trajectories and optical flow are continuous-valued variables, whereas behaviors such as start/stop, split/merge, and move along a straight line are discrete-valued. The activity structure, that is, the number of behaviors and their sequence, may not be known a priori. Many existing approaches assume that the structure of activities is known; and a fixed number of free parameters is determined based on experience or by estimating the model order. We present a Viterbi-based algorithm to estimate the switching times between behaviors and demonstrate the usefulness of the proposed models for temporal segmentation and anomaly detection. We use the notation xtt to denote the sequence {x(t1), x(t1 + 1), . . . , x(t2)}
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