Paper
Adaptive loitering anomaly detection based on motion states
Published Sep 27, 2024 · Hongjun Li, Xiezhou Huang
Multimedia Tools and Applications
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Abstract
In the field of video surveillance security in public places, loitering anomaly detection is a very critical part. Currently, the detection targets often face difficulties in loitering anomaly detection due to the complexity of the public scene, the density of pedestrians, or the varied motion states of the detection targets. This paper designs a self-adaptive loitering detection model based on motion states, and proposes a tracking method that combines local features with spatial information to enhance the correlation between adjacent frames of the same target. This model effectively reduce the impact of interleaving, occlusion, and out-of-frame situations that often occur in public places. In the detector part, a model combining frame-by-frame queue storage and self-adaptive loitering detection algorithm based on motion states is designed to solve the problem of frequent changes in motion states leading to the failure to detect loitering anomalies and to more accurately locate loitering abnormal frames. The loitering anomaly detection algorithm in this paper was tested on the IITB-Corridor dataset. Compared with the Learning regularity in skeleton trajectories (LRST) model the accuracy is improved by 4.7%, the recall is improved by 5.6%, and the precision is improved by 0.2%. The overall performance of loitering anomaly detection is improved.
This paper presents a self-adaptive loitering detection model based on motion states, improving overall performance in video surveillance security by 4.7%, 5.6%, and 0.2% compared to the LRST model.
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