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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. [ABSTRACT FROM AUTHOR]

3D depth data, especially dynamic 3D depth data, offer several advantages over traditional intensity videos for expressing objects׳ actions, such as being useful in low light levels, resolving the silhouette ambiguity of actions, and being color and texture invariant. With the wide popularity of somatosensory equipment (Kinect for example), more and more dynamic 3D depth data are shared on the Internet, which results in an urgent need to retrieve these data efficiently and effectively. In this paper, we propose a generalized strategy for dynamic 3D depth data matching and apply this strategy in action retrieval task. Firstly, an improved 3D shape context descriptor (3DSCD) is proposed to extract features of each static depth frame. Then we employ dynamic time warping (DTW) to measure the temporal similarity between two 3D dynamic depth sequences. Experimental results on our collected dataset consisting of 170 dynamic 3D depth video clips show that the proposed 3DSCD has a rich descriptive power on depth data and that the method using 3DSCD and DTW achieves high matching accuracy. Finally, to address the matching efficiency problem, we utilize the bag of word (BoW) model to quantize the 3DSCD of each static depth frame into visual word packages. So the original feature matching problem is simplified into a two-histogram matching problem. The results demonstrate the matching efficiency of our proposed method, while still maintaining high matching accuracy. [ABSTRACT FROM AUTHOR]