Collective motion pattern inference via Locally Consistent Latent Dirichlet Allocation

NEUROCOMPUTING

卷:184

Article

Zou, Jialing¹  Ye, Qixiang¹  Cui, Yanting¹  Wan, Fang¹  Fu, Kun²  Jiao, Jianbin¹ 

[1] Univ Chinese Acad Sci, Beijing 101408, Peoples R China

[2] Chinese Acad Sci, Inst Elect, Beijing 100864, Peoples R China

关键词: Collective motion;    Local motion pattern;    Topic model;    Trajectory clustering;   

DOI  :  10.1016/j.neucom.2015.08.108

来源: Elsevier

【 摘 要 】

Extracting motion descriptors in crowd videos is highly challenging due to scene clutter and serious occlusions. In this paper, Locally Consistent Latent Dirichlet Allocation (LC-LDA) model is proposed to learn collective motion patterns using tracklets and bag-of-words as low level features. The LC-LDA model implements a graph Laplacian operator to impose neighboring constraints to tracklets on a local manifold, which enforces the spatial-temporal coherence of tracklets in a high dimensional bag-of-word feature space. With initialization of clustering on a manifold, LC-LDA model improves the unsupervised inference capability and compactness of learned collective motion patterns. Experimental results on three public datasets indicate that LC-LDA based motion patterns can improve the trajectory clustering performance effectively. (C) 2015 Elsevier B.V. All rights reserved.

【 授权许可】

Free