Modularized Framework with Category-Sensitive Abnormal Filter for City Anomaly Detection
Authors: 
Jie Wu, Yingying Li, Wei Zhang, Yi Wu, Xiao Tan, Hongwu Zhang, Shilei Wen, Errui Ding, Guanbin LiAuthors Info & Claims
Jie Wu, Yingying Li, Wei Zhang, Yi Wu, Xiao Tan, Hongwu Zhang, Shilei Wen, Errui Ding, Guanbin LiAuthors Info & ClaimsPages 4669 - 4673
Abstract
Anomaly detection in the city scenario is a fundamental computer vision task and plays a critical role in city management and public safety. Although it has attracted intense attention in recent years, it remains a very challenging problem due to the complexity of the city environment, the serious imbalance between normal and abnormal samples, and the ambiguity of the concept of abnormal behavior. In this paper, we propose a modularized framework to perform general and specific anomaly detection. A video segment extraction module is first employed to obtain the candidate video segments. Then an anomaly classification network is introduced to predict the abnormal score for each category. A category-sensitive abnormal filter is concatenated after the classification model to filter the abnormal event from the candidate video clips. It is helpful to alleviate the impact of the imbalance of abnormal categories in the test phase and obtain more accurate localization results. The experimental results reveal that our framework obtains a 66.41 MF1 in the test set of the CitySCENE Challenge 2020, which ranks first in the specific anomaly detection task.
Supplementary Material
Anomaly detection in the city scenario is a fundamental computer vision task and plays a critical role in city management and public safety. Although it has attracted intense attention in recent years, it remains a very challenging problem. In this paper, we propose a modularized framework to perform general and specific anomaly detection. The experimental results reveal that our framework obtains a 66.41 MF1 in the test set of the CitySCENE Challenge 2020, which ranks first in the specific anomaly detection task.\r\n
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Information
Published In
October 2020
4889 pages
ISBN:9781450379885
DOI:10.1145/3394171
- General Chairs:
- Chang Wen Chen,
- Rita Cucchiara,
- Xian-Sheng Hua,
- Program Chairs:
- Guo-Jun Qi,
- Elisa Ricci,
- Zhengyou Zhang,
- Roger Zimmermann
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Publication History
Published: 12 October 2020
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- Short-paper
Funding Sources
- Guangdong Basic and Applied Basic Research Foundation
- State Key Development Program
- National Natural Science Foundation of China
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MM '20
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