2020/11/24 by Muhammad Zaigham Zaheer, Arif Mahmood, Zaheer, Muhammad Zaigham +6 · 10 citations
Computer Science · Engineering · #Anomaly Detection Techniques and Applications #Artificial Immune Systems Applications #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Network Security and Intrusion Detection #cs.AI #cs.CV
paper · pdf · doi:10.48550/arxiv.2011.12077
Presented in the European Conference on Computer Vision ECCV 2020. (Changes from actual paper: 1) Recently published methods have been added in ShanghaiTech and UCF Crime comparison tabs. 2) Due to some error in arxiv compilation, few references are exceeding the paragraph. Also, word 'normalcy' in the title is misspelling despite being correct in the code. (Contents are intact)
openalex publication_date 2020/11/24 · arxiv created 2021/08/04 · arxiv updated 2021/08/05 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
Learning to detect real-world anomalous events through video-level labels is a challenging task due to the rare occurrence of anomalies as well as noise in the labels. In this work, we propose a weakly supervised anomaly detection method which has manifold contributions including1) a random batch based training procedure to reduce inter-batch correlation, 2) a normalcy suppression mechanism to minimize anomaly scores of the normal regions of a video by taking into account the overall information available in one training batch, and 3) a clustering distance based loss to contribute towards mitigating the label noise and to produce better anomaly representations by encouraging our model to generate distinct normal and anomalous clusters. The proposed method obtains83.03% and 89.67% frame-level AUC performance on the UCF Crime and ShanghaiTech datasets respectively, demonstrating its superiority over the existing state-of-the-art algorithms.