Design And Implementation Of Real-time Object Tracking Algorithms Based On Complex Conditions

Object tracking is a key issue and challenging basic task in the field of computer vision,which aims at locating the object of interest in the sequences of images,and continuing to track it stably and accurately.Object tracking has a wide range of applications in many fields,such as military guidance?video surveillance?intelligent transportation?human-computer interaction?auto-driving.Researchers have proposed a variety of excellent tracking algorithms with a great improvement in precision and speed,however,in some complex scenarios,such as complex background?scale changes?deformation?occlusion and leaving the field of view,tracking algorithms always will be failed.Because of good performance and high precision,correlation filter-based tracking algorithms attract a great attention in recent years.In this paper,we propose several improved algorithms based on the deep research and analysis of correlation filter-based tracking algorithms to address some tracking problems in complex scenarios.(1)To address the shortcoming of existing correlation filter-based trackers in complex background(Background clutter?similar object disturbance).On the basis of DCF algorithm,a improved real-time correlation filter-based tracking algorithm based on complex background-aware is proposed(BSDCF).It adaptively searches similar image patches and adds them into the training process as negative samples.To adapt scale changes which normally exist,then we introduce a simple but effective scale estimation method.The experiments show that the proposed improved algorithm improves 18.0% and 16.0% in success rate and precision rate and can run 83.2 fps,which effectively improve tracking precision in complex background.(2)To address different degree of occlusion problems which may occur in the tracking process of correlation filter-based algorithms.In the basis of DCF,a improved real-time correlation filter-based algorithms is proposed based on the analysis of occlusion(DSDCF).It calculates the value of PSR and TSC to estimate the degree of occlusion.When heavy occlusion occurs,we introduce a coarse-to-fine re-detect method.It extracts several candidate image paths where object may re-occur to do rough detection.Then doing precise detection according to the result of rough detection.The experiments show that the proposed improved algorithm improves 12.5% and 10.7% in success rate and precision rate and can run 54.8 fps which effectively improve tracking precision in occlusion.(3)We take a implementation of the improved algorithm(DSDCF)with gray feature and liner kernel in DM6437 platform.Some specific optimizations have been taken.The results of experiments show that this system can track regular targets in real-time.