Conference proceeding
AnoPCN: Video Anomaly Detection via Deep Predictive Coding Network
Proceedings of the 27th ACM International Conference on Multimedia, pp.1805-1813
ACM Conferences
MM '19: The 27th ACM International Conference on Multimedia
10/15/2019
DOI: 10.1145/3343031.3350899
Abstract
Video anomaly detection is a challenging problem due to the ambiguity and complexity of how anomalies are defined. Recent approaches for this task mainly utilize deep reconstruction methods and deep prediction ones, but their performances suffer when they cannot guarantee either higher reconstruction errors for abnormal events or lower prediction errors for normal events. Inspired by the predictive coding mechanism explaining how brains detect events violating regularities, we address the Anomaly detection problem with a novel deep Predictive Coding Network, termed as AnoPCN, which consists of a Predictive Coding Module (PCM) and an Error Refinement Module (ERM). Specifically, PCM is designed as a convolutional recurrent neural network with feedback connections carrying frame predictions and feedforward connections carrying prediction errors. By using motion information explicitly, PCM yields better prediction results. To further solve the problem of narrow regularity score gaps in deep reconstruction methods, we decompose reconstruction into prediction and refinement, introducing ERM to reconstruct current prediction error and refine the coarse prediction. AnoPCN unifies reconstruction and prediction methods in an end-to-end framework, and it achieves state-of-the-art performance with better prediction results and larger regularity score gaps on three benchmark datasets including ShanghaiTech Campus, CUHK Avenue, and UCSD Ped2.
Details
- Title: Subtitle
- AnoPCN: Video Anomaly Detection via Deep Predictive Coding Network
- Creators
- Muchao Ye - Chinese Academy of SciencesXiaojiang Peng - Chinese Academy of SciencesWeihao Gan - Group SenseWei Wu - Group SenseYu Qiao - Chinese University of Hong Kong, Shenzhen
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of the 27th ACM International Conference on Multimedia, pp.1805-1813
- Conference
- MM '19: The 27th ACM International Conference on Multimedia
- Publisher
- ACM
- Series
- ACM Conferences
- DOI
- 10.1145/3343031.3350899
- Language
- English
- Date published
- 10/15/2019
- Academic Unit
- Computer Science
- Record Identifier
- 9984696569402771
Metrics
2 Record Views