Unsupervised behavior sequence segmentation based on segmental-DTW

Abstract

Behavior sequence segmentation is the first and most fundamental step of behavior analysis and recognition. In this paper, a novel unsupervised algorithm for behavior sequence segmentation is proposed. The algorithm consists of the following steps: (1) The video sequence is coarsely segmented into equal length subsequences with overlapping time window; (2) Segmental-DTW is used to find out matching behavior clips between pairs of video subsequences; (3) The similarity between behavior clips is represented by an adjacency graph, and an efficient graph clustering algorithm is used to generate behavior clusters. The algorithm, based on a coarse-to-fine strategy, is able to satisfactorily segment behavior sequences and cluster typical behavior patterns. The segmentation results can be used for further behavior modeling and recognition. Experimental results show the behavior clips segmented by this algorithm are prototypical and meaningful.