Multi-Target Irregular Behavior Recognition of Chemical Laboratory Personnel Based on Improved DeepSORT Method

The lack of safety awareness and the irregular behavior of chemical laboratory personnel are major contributors to laboratory accidents which pose significant risks to both the safety of laboratory environments and the efficiency of laboratory work. These issues can lead to accidents, equipment damage, and jeopardize personnel health. To address this challenge, this study proposes a method for recognizing irregular behavior in laboratory personnel by utilizing an improved DeepSORT algorithm tailored to the specific characteristics of a chemical laboratory setting. The method first extracts skeletal keypoints from laboratory personnel using the Lightweight OpenPose algorithm to locate individuals. The enhanced DeepSORT algorithm tracks human targets and detects the positions of the relevant objects. Finally, an SKPT-LSTM network was employed to integrate tracking data for behavior recognition. This approach was designed to enhance the detection and prevention of unsafe behaviors in chemical laboratories. The experimental results on a self-constructed dataset demonstrate that the proposed method accurately identifies irregular behaviors, thereby contributing to the reduction in safety risks in laboratory environments.