Crowded abnormal detection based on mixture of kernel dynamic texture
Shishi Duan, Xiangyang Wang, Xiaoqing Yu · 2014
A novel method for anomaly detection in crowded scenes is presented. In our method, a new feature which named Mixture of Kernel Dynamic Texture was used for video representation. The MKDT method jointly models the appearance and dynamics of the scene. Based on this method, the abnormal detection includes temporal detection and spatial detection. The model for normal crowd behavior is based on MKDTs and outliers under this model are labeled as anomalies detection. Temporal anomalies are the events with low probability under the MKDT models. While spatial detection based on discriminant saliency is used to get a spatial detection map. The proposed representation is shown to outperform various state of the art abnormal detection methods.