Robust individual and holistic features for crowd scene classification. (October 2016)

• Record Type: Journal Article
• Authors: Liu, Wenxi; Lau, Rynson W.H.; Manocha, Dinesh
• Is Part Of: Pattern recognition. Volume 58(2016:Oct.)
• Page Start: 110
• Page End: 120
• Publication Date: 2016-10
• Subjects: Crowd analysis -- Crowd scene classification -- Crowd modeling; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2016.03.031
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: In this paper, we present an approach that utilizes multiple exemplar agent-based motion models (AMMs) to extract motion features (representing crowd behaviors) from the captured crowd trajectories. In the exemplar-based framework, we propose an iterative optimization algorithm to measure the correlation between any exemplar AMM and the trajectory data. It is based on the Extended Kalman Smoother and KL-divergence. In addition, based on the proposed correlation measure, we introduce the novel individual feature, in combination with the holistic feature, to describe crowd motions. Our results show that the proposed features perform well in classifying real-world crowd scenes.