| Abstrakt: |
Normal behavior is usually more common than abnormal behavior, leading to the problem of category imbalance in the dataset. This can lead to models being biased towards normal behaviors that are easier to identify and less accurate in identifying abnormal behaviors. Meanwhile, the number of people may change in different video frames, and such changes in the number of people can lead to a decrease in the adaptability of most models. To improve the accuracy of abnormal behavior recognition in multi-person scenarios, this paper proposes an intelligent recognition framework based on skeleton pose estimation and person detection, which integrates a pose estimation module, You Only Look Once Human Pose (YH-Pose), and a behavior classification module, Bidirectional Recognition Long Short Term Memory (BR-Lstm). Firstly, the YH-Pose module predicts the 2D positions of human skeletal joint points in the video frames. Secondly, the BR-Lstm module takes as input the skeleton coordinates detached encoding to generate behavioral feature vectors and global feature representations. Finally, a classifier classifies the behavior into normal and abnormal. The proposed framework was experimented on two publicly available datasets: HMDB51 and NTU_RGB+D. The experimental results show that the accuracy of behavioral recognition is 51.3% and 96.7%, respectively, which is better than the mainstream model. The proposed framework was also evaluated with actual surveillance video data. The experimental results showed that the framework could detect five abnormal behaviours on traffic roads: fall , kicking, punching, vomiting and looking down at the mobile phone. The code is avaliable at https://github.com/3083156185/MP-Abr.git. [ABSTRACT FROM AUTHOR] |
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