Detecting Coherent Groups in Crowd Scenes by Multiview Clustering

作者：Wang, Qi^*; Chen, Mulin; Nie, Feiping; Li, Xuelong

来源：IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(1): 46-58.

DOI：10.1109/TPAMI.2018.2875002

• Feature extraction
• Clustering methods
• Optical imaging
• Electronic mail
• Videos
• Computer science
• Correlation
• Crowd analysis
• group detection
• context descriptor
• multiview clustering
• graph clustering

Detecting coherent groups is fundamentally important for crowd behavior analysis. In the past few decades, plenty of works have been conducted on this topic, but most of them have limitations due to the insufficient utilization of crowd properties and the arbitrary processing of individuals. In this study, a Multiview-based Parameter Free framework (MPF) is proposed. Based on the L1-norm and L2-norm, we design two versions of the multiview clustering method, which is the main part of the proposed framework. This paper presents the contributions on three aspects: (1) a new structural context descriptor is designed to characterize the structural properties of individuals in crowd scenes; (2) a self-weighted multiview clustering method is proposed to cluster feature points by incorporating their orientation and context similarities; and (3) a novel framework is introduced for group detection, which is able to determine the group number automatically without any parameter or threshold to be tuned. The effectiveness of the proposed framework is evaluated on real-world crowd videos, and the experimental results show its promising performance on group detection. In addition, the proposed multiview clustering method is also evaluated on a synthetic dataset and several standard benchmarks, and its superiority over the state-of-the-art competitors is demonstrated.

• 出版日期2020-1-1
• 单位西北工业大学