 |
    |
Finding Coherent Motions and Semantic Regions in Crowd Scenes: A Diffusion and Clustering Approach
Weiyue Wang1, Weiyao Lin1, Yuanzhe Chen1, Jianxin Wu2, Jingdong Wang3, and Bin Sheng4
1Dept. Electronic Engr., Shanghai Jiao Tong Univ., China
2National Key Laboratory for Novel Software Technology, Nanjing Univ., China
3Microsoft Research, Beijing, China
4Dept. Computer Science & Engr., Shanghai Jiao Tong Univ., China
Abstract. This paper addresses the problem of detecting coherent motions in crowd scenes and subsequently constructing semantic regions for activity recognition. We first introduce a coarse-to-fine thermal-diffusion-based approach. It processes input motion fields (e.g., optical flow fields) and produces a coherent motion filed, named as thermal energy field. The thermal energy field is able to capture both motion correlation among particles and the motion trends of individual particles which are helpful to discover coherency among them. We further introduce a two-step clustering process to construct stable semantic regions from the extracted time-varying coherent motions. Finally, these semantic regions are used to recognize activities in crowded scenes. Experiments on various videos demonstrate the effectiveness of our approach.
LNCS 8689, p. 756 ff.
lncs@springer.com
© Springer International Publishing Switzerland 2014