Analysis of moving cluster with scene constraints for group behavior pattern mining

Abstract

Group behavior pattern mining in traffic scenarios is a challenging problem due to group variability and behavioral regionality. Most methods are either based on trajectory data stored in static databases regardless of the variability of group members or do not consider the influence of scene structures on behaviors. However, in traffic scenarios, information about group members may change over time, and objects' motions show regional characteristics owing to scene structures. To address these issues, we present a general framework of a moving cluster with scene constraints (MCSC) discovery consisting of semantic region segmentation, mapping, and an MCSC decision. In the first phase, a hidden Markov chain is adopted to model the evolution of behaviors along a video clip sequence, and a Markov topic model is proposed for semantic region analysis. During the mapping procedure, to generate snapshot clusters, moving objects are mapped into the corresponding sets of moving objects according to the semantic regions where they are located at each timestamp. In the MCSC decision phase, a candidate MCSC recognition algorithm and screening algorithm are designed to incrementally identify and output MCSCs. The effectiveness of the proposed approach is verified by experiments carried out using public road traffic data.