Batch feature standardization network with triplet loss for weakly-supervised video anomaly detection
作者:
Highlights:
• A batch feature standardization module facilitates video anomaly detection.
• Research focus shifts from instance-level and bag-level relationships to batch-level associations.
• A novel strategy is used to introduce triplet loss in video anomaly detection.
• The method achieves comparable performance with the state-of-the-art approaches on several datasets.
摘要
•A batch feature standardization module facilitates video anomaly detection.•Research focus shifts from instance-level and bag-level relationships to batch-level associations.•A novel strategy is used to introduce triplet loss in video anomaly detection.•The method achieves comparable performance with the state-of-the-art approaches on several datasets.
论文关键词:Video anomaly detection,Batch feature standardization,Triplet loss,Feature processing
论文评审过程:Received 29 September 2021, Revised 12 January 2022, Accepted 26 January 2022, Available online 31 January 2022, Version of Record 15 February 2022.
论文官网地址:https://doi.org/10.1016/j.imavis.2022.104397