Optimal Analysis of Target Dynamic Tracking Strategy Based on Computer Vision
Computer vision is a highly intensive interdisciplinary subject which combines artificial intelligence and advanced algorithms, and its target tracking has become a hot research topic at present. At the operational level of target tracking, PF(Particle Filter) technology has its own technical advantages in processing image interpretation interference and target movement change. On the one hand, BPNN(Back Propagation Neural Network) technology is introduced to improve the diversity of particles and improve the accuracy of target information at the next moment so as to meet the dual requirements of dynamic intelligent capture and timeliness at the next moment of target tracking; on the other hand, PSO(Particle Swarm Optimization) technology is introduced to prevent particle degradation and quickly find the global optimal solution of particle information. The simulation results show that the method of using BPNN PSO to improve the particle filter algorithm, not only improves the accuracy of target tracking information, but also reduces the timeconsuming of target tracking.
2019 3rd International Conference on Electronic Information Technology and Computer Engineering (EITCE)