Research On Object Tracking Based On TLD And Compressive Sensing

Object tracking has always been an important subject in computer vision,which can be applied to many domains,such as video surveillance,aeronautical detection,human-computer interaction and so on.Although there have been new algorithms proposed,it is still a challenging and practical task to develop effective and efficient tracking algorithm due to the undetermined factors such as illumination variation,pose change,scale variation and occlusion during tracking.Aiming the object tracking under complicated environment,this paper places emphasis on TLD^[1]?Tracking Learning Detection?and compressive tracking^[2].By combining the advantages of both methods,an effective tracking algorithm is proposed based on TLD framework and compressive sensing theory.The main research achievements include following aspects:?1?Modify the median-flow tracking algorithm by replacing uniformly distributed interest points with FAST feature points and using neighborhood consistency constraint to filter tracking failures in order to improve the performance of tracker.?2?Estimate the object motion using Kalman filter and determine the search region adaptively with the confidence probability of the object window to reduce the search area of the detector for improving detection efficiency on the premise of tracking accuracy.?3?Employ compressive sensing in TLD framework,and improve compressive tracking based on PN?Positive and Negative?learning^[3]and scale information from tracker.Moreover,use improved compressive sensing combined with nearest neighbor classifier to improve the detection stability.By combining advantages of the TLD and compressive tracking,the modified tracking algorithm improves the tracking stability and accuracy to some extent.The experiments show that,compared to original tracking methods,the improved algorithm can locate the object more accurately and stably,taking efficiency into consideration.