Fast detection method for pedestrian video abnormal behavior based on keyframe extraction and multi-task mixed model
Huiyu Mu, Jiangwei Li, Jiashuai Su, Luhui Wang, Junfeng Tian, Lanxue Dang · 2024
In recent years, many video anomaly detection methods have mainly used reconstruction and prediction based methods. However, due to the powerful encoding and decoding capabilities of autoencoders in reconstruction methods, the misjudgment rate of abnormal samples is high, and prediction methods are easily affected by environmental changes and data noise. Real time and accurate detection of pedestrian abnormal events still faces huge challenges. This article proposes a fast method VAD-KEMM for detecting abnormal behavior, which uses keyframe extraction and a multi task hybrid model. Firstly, key frames are extracted through segmented clustering and inter frame differences to improve detection efficiency; Then, a dual branch hybrid model is constructed using human skeletal information to improve reconstruction accuracy, and multi task learning is used to enhance prediction ability. The experimental results show that the AUC values of this method on the HR Shanghai Tech and HR Avenue datasets are 76.8% and 87.1%, respectively, indicating high detection efficiency and accuracy.