Sparse representation of large scene video image motion object tracking simulation
In large scene videos,there is a large amount of complex background information,which is similar to the appearance of the target,so it is difficult to accurately track the target.To solve this problem,this article put for-ward a method for tracking motion targets in large scene video images based on sparse representation.Firstly,Haar-like features were extracted from video images and compressed.Secondly,background information was incorporated in-to the dictionary to construct an over-complete dictionary.Then,the traditional particle filter was improved.Mean-while,an Unscented particle filter algorithm was proposed based on residual error.Moreover,a discriminant function was added to the traditional sparse representation,so that a discriminant sparse representation was generated.Based on L1 norm minimization,the sparse coefficients of the candidate target were solved.Under continuous dictionary up-dates,the motion target tracking was realized through the residual-based Unscented particle filter algorithm.Experi-mental results show that the target tracking accuracy and coincidence rate are more than 0.9,and the average tracking time was short.
Sparse representationLarge scene videoMoving object trackingParticle filtering
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