Cross-scene abnormal event detection

This paper presents an cross-scene abnormal event detection method by adopting Bag of Words (BoW) model with Spatial Pyramid Matching Kernel (SPM) cooperating with SIFT features and a SVM classifier. Different from existing abnormal event detection methods where abnormal events happened in a well-learned scene are considered and detected, we aim to detect concerned events in public where scenes can be unlearned before. Our method is motivated by the fact that the pattern of the notable events are similar and the learned models should be transferable to examine the events in other unlearned public scenes. To learn the patterns for an abnormal event, we divide the proposed method into two steps: feature coding and spatial pooling. For the feature coding step, the codebook is generated and the feature is quantized based on small patches. For the spatial pooling step, the patches are concatenating to exploit the spatial information of local regions. The intersection kernel is used to integrate with a SVM classifier. Experimental results on two benchmark databases demonstrate the efficacy of our proposed approach.