Research On Visual Tracking Algroithm Based On Deep Learning

As one of the most important parts in the field of computer vision research today,Visual Target Tracking needs the comprehensive application of knowledge in all fields.Only in this way can we respond promptly and achieve the goal of robust tracking in unexpected situations.The current depth learning tracking algorithm oftenly uses only the final general target to establish the reliability,though ignoring network intermediate features and multi-tracking method.So that how to combine network and tracking process more meticulously and more comprehensively to increase the performance of the algorithm,is this article's main research aspect.The primary research of this article is showing as below:(1)A novel RGBD and sparse learning-based tracker is proposed.Firstly,detecting the obstructions in target area by using depth data information based under tracking framework of sparse learning,and applying detected obstructions region as a model to generate obstruction template;Secondly,combining depth image features with visual features based on color image,to be used for robust feature description of target appearance;Thirdly,to prevent from incorrectly updating template by establishing a obstruction detection method based on depth histogram analysis.According to experimental indications of Princeton data set,this algorithm is able to achieve a better tracking effort than the currently porpular tracking algorithm.(2)A L1 tracking algorithm based on depth learning is proposed.To structure a training sample from an area that target can be easily distinguished in a vedio recording scene by using fixed camera,and to structure a two-way symmetric and weight-sharing deep CNN.This chapter achieves the purpose of increasing robustness under the framework of L1 tracking system,by applying trained depth network to extract candidate features of target to conduct sparse representation,deal with the problem of covering or change in illumination.And according to experimental indications,this chapter provides an algorithm that has a better overall effect than those existing algorithms.(3)A tracking algorithm based on multilayered PCA convolution filter is proposed.Using PCA first to make PCA feature vectors of the slice image data set as a convolution filter,then further achieves target tracking by using feature matching and particle filter.Experimental simulation results indicate that this algorithm has an outstanding immutability when facing severe lighting and object covering,etc,and also show a good robustness in overall effect.