Anomaly Detection Based on Stacked Sparse Coding With Intraframe Classification Strategy

Abstract

Anomaly detection in videos is still a challenging task among the computer vision community. In this paper, an efficient anomaly detection method based on stacked sparse coding (SSC) with intraframe classification strategy is proposed. Each video is divided into blocks and the Foreground Interest Point (FIP) descriptor is proposed to describe the appearance and motion features for each block. The spatial-temporal features are then encoded with SSC. Specifically, the first stage of SSC encodes the spatial connections among blocks and the second stage of SSC encodes the temporal connections of all frame patches in each block. Finally, an intraframe classification strategy which uses the probabilistic outputs of SVM is proposed to evaluate the abnormality of each block. Contributions of this paper are listed as follows: 1) The FIP descriptor is proposed to describe the features of blocks, which reserves more spatial-temporal information. 2) The SSC encoding method encodes both the spatial and temporal connections of blocks, which makes the features more representative. 3) The intraframe classification strategy keeps the evaluation consistency among blocks and it helps to improve detection performance. The proposed method is examined on four public datasets with different background complexities and resolutions: UCSD Ped1 dataset, UCSD Ped2 dataset, Avenue dataset, and Subway dataset. The results are further compared with previous approaches to confirm the effectiveness and advantages of this method.