Abstract
Recently,real-time monitoring multi-target tracking as an important component of intelligent transportation system(ITS) has been paid much attention.The traditional multitarget tracking algorithm has problems that the processing speed is slow and the false matches may happen when vehicles cross.Firstly,the algorithm detects moving targets through modeling a complex background based on Bayesian rules,then introduces a multi-target tracking algorithm based on mean shift particle filter(MSPF).Firstly,the algorithm predicts the extent possible by the use of MSPF for each vehicle in the next frame,uses different detection strategies for simple or multiple targets to avoid a global search and improve the tracking speed;by constructing the importance density function based on the latest observations,the algorithm can achieve an accurate and robust tracking in the part of the block and cross-vehicle.Simulation results verify the proposed algorithm.
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