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Opto‐electric target tracking algorithm based on local feature selection and particle filter optimization
Author(s) -
Peng Liangqing,
Wu Yanpeng,
Huang Lei
Publication year - 2018
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4670
Subject(s) - artificial intelligence , weighting , computer vision , tracking (education) , feature (linguistics) , particle filter , computer science , mean shift , histogram , pattern recognition (psychology) , stability (learning theory) , filter (signal processing) , image (mathematics) , medicine , psychology , pedagogy , linguistics , philosophy , machine learning , radiology
Summary Aiming at exploring the opto‐electric target tracking, which is an important technology in the field of computer vision, the binocular stereo vision camera opto‐electric target tracking is studied and and a multi feature fusion characterization modeling method locally weighted is proposed. The target area is divided into multiple sub‐image areas by the modeling method, the feature histogram after the background weighting is extracted, and the sub‐image region is taken as a basic unit for adjusting the feature weight. The sub‐image area selected is regarded as the significant area, and the significant area is further extracted and fused in particle filter tracking algorithm. Then, the obtained significant are is conducted with color distribution processing. In the state prediction stage, the Mean Shift algorithm is applied to optimize each particle so that it converges to the optimal position. The experiment results showed that the multi feature fusion representation modeling method has better tracking accuracy and stability compared with the traditional fusion method and after the color distribution treatment; it has strong anti‐jamming for background effect. It is concluded that using Mean Shift algorithm for particle optimization can further strengthen the accurate tracking of the targets.