Intelligent early warning technology for illegal behaviors in work scenarios based on integrated video perception

Artificial intelligence often faces the challenge of balancing real-time performance and warning accuracy when processing dynamic job scene videos. Therefore, this article proposes an intelligent warning technology for violations based on integrated video perception. Firstly, a video perception integrated frame sampling strategy is adopted to obtain video data, and a quantifiable personnel violation risk warning index system is constructed. At the methodological level, human keypoint detection technology is introduced, and behavior feature vectors are constructed based on Manhattan distance and skeletal angles. A dynamic visual model is constructed by integrating video features, and anomaly recognition and warning triggering are achieved through comparison of position difference and threshold. The experimental results show that this technology can effectively identify various violations such as "illegal operation of machinery", and the accuracy of key point warning reaches 82.7% -89.1%, which is significantly better than existing literature methods; It also performs better in terms of missed detection rate, verifying its high reliability and practicality in complex work scenarios.