A hybrid swarm intelligence based approach for abnormal event detection in crowded environments

Abstract In this paper, we propose a hybrid swarm intelligence based approach to tackle the problem of abnormal event detection in crowded environments in surveillance videos. In the proposed approach, a video frame is subjected to a series of operations to extract most salient information from it. Initially, a novel discriminative 2D variance plane corresponding to (and equal in dimensions to) each video frame is constructed in which the value at each pixel location represents the variance of optical flow field magnitude in the local spatio-temporal neighborhood of that pixel. Consequently, a modified ant colony optimization (ACO) clustering algorithm is employed to partition the 2D variance plane into salient and non-salient clusters. The cluster with salient pixels represents the regions of a video frame where optical flow variations inside the local spatio-temporal neighborhood of a pixel are high and is selected for further computation. Finally, a novel predator-prey algorithm is implemented and predators are advected over the prey values in the selected cluster to compute histogram of swarms (HOS) for a particular frame. The proposed approach outperforms state of the art on two commonly used datasets in our experiments, i.e., UMN crowd anomaly dataset and UCF web dataset.