An adaptive clustering approach for group detection in the crowd

Abstract

Collective motion groups play an important role in pedestrian crowd analysis and social event detection. As the basis of group modeling in the crowd, a collective motion group detection algorithm is proposed in this paper. Compared to other state-of-the-art group detection achievements, ours is more robust in complex crowded motion scenes, involving varieties of random traffics and different motion types. First of all, we introduce an automatic foreground detection strategy, and then generate dense tracklets by tracking on salient points in foreground area for preprocessing. Salient point tracklets are represented by spatio-temporal features afterwards. By exploiting an adaptive initiation clustering technique, a hierarchical clustering model is built to partition the crowd into groups depending on different features layer by layer. We demonstrate the effectiveness and robustness of our algorithm quantitatively and qualitatively on various real crowd videos.