Opto-electric target tracking algorithm based on local feature selection and particle filter optimization

摘要

Aiming at exploring the opto-electric target tracking, which is an important technology in the fieldof computer vision, the binocular stereo vision camera opto-electric target tracking is studiedand and a multi feature fusion characterization modeling method locally weighted is proposed.The target area is divided into multiple sub-image areas by themodeling method, the feature histogramafter the background weighting is extracted, and the sub-image region is taken as a basicunit for adjusting the feature weight. The sub-image area selected is regarded as the significantarea, and the significant area is further extracted and fused in particle filter tracking algorithm.Then, the obtained significant are is conductedwith color distribution processing. In the state predictionstage, the Mean Shift algorithm is applied to optimize each particle so that it convergesto the optimal position. The experiment results showed that the multi feature fusion representationmodelingmethod has better tracking accuracy and stability compared with the traditionalfusion method and after the color distribution treatment; it has strong anti-jamming for backgroundeffect. It is concluded that usingMeanShift algorithm for particle optimization can furtherstrengthen the accurate tracking of the targets.