基于Segmental-DTW的无监督行为序列分割

行为序列分割是行为分析与识别中最初始、最基础的一个步骤.提出了一种无监督的行为序列分割算法,主要步骤包括:(1)采用等长有重叠的时间窗口对视频序列进行粗分割;(2)将粗分割的视频段两两作比较,通过Segmental-DTW算法分割出两个视频段中最相似的行为片断;(3)将行为片断的相似性转化为邻接图表示,通过图聚类方法对分割出的行为片断进行聚类.该算法采用了从粗到细的分割思想,能够准确地分割出视频序列中大量出现的行为的片断,并将相同行为的片断聚为一类.分割结果可以直接用于行为建模和识别.实验结果也表明了分割出的行为片断具有较好的代表性和有效性.

Unsupervised Behavior Sequence Segmentation Based on Segmental-DTW

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.