Behavior Recognition Based on Category Subspace in Crowded Videos

• Published in: IEEE access Vol. 8; pp. 222599 - 222610
• Main Authors: Deng, Chunhua, Kang, Xiaoge, Zhu, Ziqi, Wu, Shiqian
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: associative subspace; Categories; Character recognition; Classification; Classifiers; Correlation; Crowd monitoring; Data integration; imbalanced samples; Multi-label; Recognition; Semantics; subspace; Subspace methods; Subspaces; Task analysis; Video data
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3043412

Crowd behavior refers to a collective behavior composed of two or more individuals who influence, interact, and depend on each other for a specific goal. Compared with an ordinary crowd behavior, the probability of a dangerous crowd behavior is much smaller. Video-based crowd behavior recognition can be categorized as one multi-label classification task, which is characterized by complex scenes and imbalanced samples. Aimed at tackling problems of imbalanced samples and multi-label task, a classification method of associative subspace is proposed. For a single category (called main category) with fewer samples, this paper generates a special subspace wherein it is relatively easy to distinguish these samples by association with other categories. A classifier that can weaken the main category and strengthen relationship between the main category and other categories is designed in the subspace. Therefore, the main category can contribute to reducing dependence on the number of samples with the above-mentioned classifier in the corresponding subspace. In order to make full use of the relevant information concerning categories, multi-label information is further injected into spatio-temporal features of video action representation. Experiments on a challenging WWW dataset show that both the proposed subspace method and multi-label information fusion mechanism are efficient.