Research On Target Tracking Algorithm Based On Subspace

Video tracking is a hot research problem in the field of computer vision,which has been widely studied by many scholars in the past few decades.Among them,the algorithm to adapt the appearance change of the object by establishing the appearance model is the key research content.Although there have been many research results about the spatial modeling,there are a lot of problems with tracking challenges remain to be solved: how to adapt to the internal factors in the process of target tracking its position change,scale change and deformation;how to solve the video of the illumination change,complex background and partial occlusion or long-time occlusion area.Therefore,it is very important to establish a good appearance model in complex environment and obtain the minimum error particles through the appearance model to achieve robust tracking of the target.The main work of this paper is as follows:(1)In order to solve the problem that the traditional incremental subspace method is easily affected by the training set,it can not be well adapted to the problem of occlusion or fast motion of the target,so the method of target tracking based on weighted incremental subspace learning is proposed.First,two similarity measurement methods are used to judge the state of the target.In the case of adding weights,the normal template set is used to track the normal target,and the abnormal template is added to the normal template to track the abnormal target.After that,the training set is selected with templates that are not obscured or less obscured,and updated according to the order of similarity with the appearance model.Experimental results show that the algorithm is more robust when targets are partially occluded,fast moving,and appearance changes.(2)In order to solve the problem that the traditional sparse subspace learning method has high computational complexity and it is easy to introduce the background information and lead to the tracking drift,the sparse subspace target tracking method based on the corner point is proposed.First,calculate the similarity between the candidate and the appearance model,and select the subspace adaptively for different similarity.In the case of high similarity,only the base vector is used to perform the error operation on the candidate target,the incremental updating base is directed to the quantum space.In the case of low similarity,the feature points are extracted by the ShiTomasi corner method,and the trivia template is set up by the image block which contains the most feature points,and the complete character is composed of the orthogonal base vector and the trivial template.In addition,we add the weights from high to low to estimate the error values and update the trivial templates.The results of qualitative and quantitative experiments show that the algorithm has better tracking effect and more practical performance in complex conditions such as occlusion,rotation,scale change,fast motion and illumination change.