Multi-Target Behavior Monitoring Analysis System and Collaborative Optimization Method Based on AI Image Recognition

Abstract

Aiming at the technical bottlenecks of existing behavior monitoring systems in complex scenarios, such as weak multi - target parallel processing capability, lack of time coordination mechanism.unreasonable computing resource allocation,and difficulty in balancing monitoring accuracy and real - time performance, this paper proposes a multi- target behavior monitoring analysis system and a full - process collaborative optimization method based on AI image recognition. Taking the standardized three - stage process of image collection–feature extraction–behavior analysis as the core, this method constructs an array - type multi - station parallel image acquisition hardware architecture.and realizes unified computing power scheduling and efficient data interaction through the central control module. A time - stage modeling strategy integrating scene complexity,target motion speed and recognition accuracy requirements is established to accurately quantify the duration of each monitoring link. A multi - target time - series collaborative scheduling rule is designed to avoid task execution phase conflicts. A full - cycle resource consumption model and a dynamic computing power balance mechanism are constructed to minimize system computing power fluctuation .Theoretical analysis and simulation verification show that the proposed system can achieve a monitoring coverage rate of 98. 7%in multi - target parallel monitoring cases lead to a 27. 3% improvement in utilization of computing resources and to an average accuracy of 95. 2% in behavior recognition. The system is more stable and more suitable for real - time use than previous approaches .From the technical standpoint .the work now offers full support—both practical and theoretical—for intelligent monitoring of people in public areas or in industry, in traffic or in parks.