Unsupervised discovery of crowd activities by saliency-based clustering
Along with the rapid development of digital information technology, video surveillance systems have been widely used in numerous public places, such as squares, shopping malls and banks, to monitor crowd in case of anomalous events. Meanwhile, great challenges have been posed to worldwide researchers because the analysis of the exponentially growing crowd activity data is an arduous task. In this paper, we develop a novel unsupervised crowd activity discovery algorithm aiming to automatically explore latent action patterns among crowd activities and partition them into meaningful clusters. Inspired by the computational model of human vision system, we present a spatio-temporal saliency-based representation to simulate visual attention mechanism and encode human-focused components in an activity stream. Combining with feature pooling, we can obtain a more compact and robust activity representation. Based on affinity matrix of activities, N-cut is performed to generate clusters with meaningful activity patterns. We carry out experiments on our HIT-BJUT dataset and the UMN dataset. The experimental results demonstrate that the proposed unsupervised discovery method is fast and capable of automatically mining meaningful activities from large-scale and unbalanced video data with mixed crowd activities.
unsupervised discovery spatio-temporal saliency crowd activity analysis spatio-temporal pooling dynamic time warping normalized cut