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Video anomaly localization using modified faster RCNN with soft NMS algorithm
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DOI：10.1007/s41060-024-00591-0.png)
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摘要
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Localization of anomalies in surveillance videos is a critical component of smart and intelligent surveillance systems. The goal of anomaly detection is to automatically detect the presence of anomalies in a short amount of time. The proposed system developed an efficient and improved faster RCNN-based system for accurate detection of anomalies. The modified version of Faster RCNN extracts the features at different levels by using a feature pyramid network and is able to detect small-scale anomalies by adding a Soft NMS algorithm. The proposed model is experimentally evaluated using three benchmarked datasets UCSD Ped1, UCSD Ped2 and Avenue, and gets better detection performance. Finally, a comparison study with different YOLO series is conducted, and our system outperforms them.
Keyword:
Anomaly detection
Smart surveillance system
Faster RCNN
Soft NMS
YOLO
期刊
I
IF:
2.8
论文数:
1.1K
被引数:
1.3K
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分享收藏Video anomaly detection and localization using motion-field shape description and homogeneity testing.png)
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