{"title":"Innovative Video Anomaly Detection: TCN-AnoDetect With Self-Supervised Feature Learning","authors":"V. Rahul Chiranjeevi, D. Malathi","doi":"10.1002/ett.70045","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>Video anomaly detection is a critical task in surveillance, industrial quality control, and anomaly monitoring systems. Recognizing anomalous events or behaviors within video sequences is challenging due to the diverse and often vague nature of anomalies. A novel temporal convolutional network-based anomaly detection (TCN-AnoDetect) is proposed that leverages TCNs and self-supervised learning. In this, TCNs are employed to model the temporal context within video sequences effectively, capturing short and long-term dependencies. The algorithm integrates TCNs with pretrained models to encode rich spatiotemporal features. The core of TCN-AnoDetect lies in self-supervised feature learning, where a neural network is pretrained on unlabeled video data to capture high-level spatiotemporal features. The anomaly detection module combines reconstruction-based and temporal context–aware approaches, using reconstruction errors and temporal context deviations for anomaly scoring and classification. To enhance model robustness, TCN-AnoDetect incorporates domain adaptation technique to handle domain shifts and evolving anomalies. The proposed algorithm is evaluated on three different benchmark datasets and ShanghaiTech Campus, demonstrating its superior performance. The extensive experiments performed in terms of different evaluation measures show the efficiency of the TCN-AnoDetect algorithm. The TCN-AnoDetect, an innovative approach, thereby provides promising solutions in video anomaly detection and in various applications.</p>\n </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 1","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transactions on Emerging Telecommunications Technologies","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/ett.70045","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"TELECOMMUNICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Video anomaly detection is a critical task in surveillance, industrial quality control, and anomaly monitoring systems. Recognizing anomalous events or behaviors within video sequences is challenging due to the diverse and often vague nature of anomalies. A novel temporal convolutional network-based anomaly detection (TCN-AnoDetect) is proposed that leverages TCNs and self-supervised learning. In this, TCNs are employed to model the temporal context within video sequences effectively, capturing short and long-term dependencies. The algorithm integrates TCNs with pretrained models to encode rich spatiotemporal features. The core of TCN-AnoDetect lies in self-supervised feature learning, where a neural network is pretrained on unlabeled video data to capture high-level spatiotemporal features. The anomaly detection module combines reconstruction-based and temporal context–aware approaches, using reconstruction errors and temporal context deviations for anomaly scoring and classification. To enhance model robustness, TCN-AnoDetect incorporates domain adaptation technique to handle domain shifts and evolving anomalies. The proposed algorithm is evaluated on three different benchmark datasets and ShanghaiTech Campus, demonstrating its superior performance. The extensive experiments performed in terms of different evaluation measures show the efficiency of the TCN-AnoDetect algorithm. The TCN-AnoDetect, an innovative approach, thereby provides promising solutions in video anomaly detection and in various applications.
期刊介绍:
ransactions on Emerging Telecommunications Technologies (ETT), formerly known as European Transactions on Telecommunications (ETT), has the following aims:
- to attract cutting-edge publications from leading researchers and research groups around the world
- to become a highly cited source of timely research findings in emerging fields of telecommunications
- to limit revision and publication cycles to a few months and thus significantly increase attractiveness to publish
- to become the leading journal for publishing the latest developments in telecommunications