Laboratory Abnormal Behavior Recognition Method Based on Skeletal Features

The identification of abnormal laboratory behavior is of great significance for the safety monitoring and management of laboratories. Traditional identification methods usually rely on cameras and other equipment, which are costly and prone to privacy leakage. In the process of human body recognition, they are easily affected by various factors such as complex backgrounds, human clothing, and light intensity, resulting in low recognition rates and poor recognition results. This article investigates a laboratory abnormal behavior recognition method based on skeletal features. One is to use Kinect sensors instead of traditional image sensors to obtain characteristic skeletal data of the human body, reducing external limitations such as lighting and increasing effective data collection. Then, the collected data is smoothed, aligned, and image enhanced using moving average filtering, Discrete Fourier Transform, and contrast, effectively improving data quality and helping to better identify abnormal behavior. Finally, the OpenPose algorithm is used to construct a laboratory anomaly behavior recognition model. OpenPose can be used to connect the entire skeleton through the relationships between points during the process of extracting human skeletal points, and combined with multi-scale pyramid networks to improve the network structure, effectively improving the accuracy and recognition speed of laboratory abnormal behavior recognition. The experiment shows that the accuracy, precision, and recall of the behavior recognition model constructed by the algorithm are 95.33%, 96.68%, and 93.77%, respectively. Compared with traditional anomaly detection methods, it has higher accuracy and robustness, lower parameter count, and higher operational efficiency.