Visual Object Tracking Based On Feature And Model Selection

The object tracking based on local feature representation has a good ability to re-sist occlusion and deformation,so it has many applications and discussions in the field of object tracking.Although object tracking based on local feature representation has many advantages,it still has the following several problems:1.The effective repre-sentation of a tracking object is a fundamental problem in the field of object tracking,and an effective target feature representation can play a decisive role in the tracking performance of a tracker.For feature representation in object tracking,there are feature representations based on global targets and feature representation based on local parts,and the localized feature representation is better than global feature representation in the case of occlusion and partial deformation when tracking an object.The traditional localized target representation is to express all the local regions in a tracking target with uniform feature representation.And they do not express distinguishingly and selective-ly based on the location information of each local feature,so that each local feature can not be well distinguish foreground from background.2.Deep neural network fea-ture have a great advantage over artificial design features.Different from the artificial design features,the deep neural network uses massive amount of data to find a higher level semantic representation of a target.However,the tracking algorithm based on lo-cal feature representation can not take the advantage of different feature maps produced by deep neural network to represent different local part in the tracked object.So it can not well use the location information of different local parts to select the best feature map to distinguish the foreground from the background.3.In the challenges of object tracking,the scale transformation of the target has always been a very important and d-ifficult one.In local target tracking algorithm,the commonly used target tracking scale transformation method can not properly solve the problem of scale change.So it can not well track the object with deformation.The last problem not only lies in the algorithm of local based object tracking,but it universally lies in the filed of object tracking.In the field of object tracking,uncer-tainties such as deformation,occlusion,blur,fast movement,scale change,etc.are the challenges that each vision tracker faces and copes with.However,due to the differ-ent mechanism of each tracker tracking,a visual tracker often can not handle all of the above uncertainties.So a single tracker can fail in the tracking object with some specific uncertainties.To deal with problem 1,an effective local feature representation is proposed.With the usage of position information of each local area,we can adaptively explore different color characteristics of each local region by using the popular Color Name representa-tion.Finally,the combination of each local feature makes an effective representation of the whole target that can well distinguish the foreground and the background,so as to enhance the overall target tracking performance.To deal with problem 2,Based on the above-mentioned adaptive local feature expression,this paper proposes a method that jointly use deep neural network feature and artificial designed feature to express the local characteristics of the target,so the performance of the object tracking is further promoted.To deal with problem 3,this paper adopts the method of bounding box re-gression in the field target detection to solve the problem of scale transformation based on local based object tracking.So the local based object tracking algorithm can also solve the transformation of the target effectively.For the last problem,we unite a num-ber of different trackers to track a target at the same time,and in each frame the "best"tracking results are selected by a sets of designed rules.We integrate the advantages of different trackers,while making up for the shortcomings of different trackers.And fi-nally we make an end to end to multi-trackers selection algorithm to improve the overall tracking performance.Experiments show that by the color feature and deep feature selection,the per-formance of local base object tracking algorithm has a remarkable promotion.By the method of bounding box regression,the scale problem in the filed local based object tracking can be well solved.Lastly,by the algorithm of muti-tracker selection,the mer-its of different trackers are combined.And the overall tracking performance are well promoted.