Meanshift Algorithm Based Visual Tracking Research And Improve

Visual tracking is one of the most important and challenging research aspect in field of computer vision.It has been successfully applied and played critical roles in multiple practical applications including intelligent video surveillance,intelligent traffic,weather analysis,remote measurement and medical image analysis.However,most of visual tracking algorisms can only achieve acceptable performance under certain circumstances,they still have much space to improve.Furthermore,the dynamic change of targets and/or background’s shape require improvements of stabilization and accuracy of visual tracking algorisms.This thesis studied the issue of visual tracking in the dynamic environment.The critical points of this issue are the detecting and tracking the variation of rigid and non-rigid objects and achieve real-time tracking.Base on the fact that the size variation and similar object occurrence in practical complex environment,Mean Shift algorithm is able to reduce the dimension of data and distinguish them at the high dimensions.Thus,this thesis focused on the major problems when using Mean Shift algorithm,which is one of Nonparametric estimations for probability.We further studied the drifting of objects in the complex circumstance,and accordingly provided the idea that further introduced the concept of Color space distance on the theory basic of kernel function calculation,achieved the effective distinguish at the high dimension,in order to deal with the problem of relative low accuracy in complex background.Comparing to traditional methods,this approach could achieve more accurate and robotic visual tracking.Considering the target shape change during visual tracking especially for moving objects,we introduced the concept of Bhattacharyya Distance to deal with this problem and combined the advantages of these two concepts to increase the accuracy and instability of visual tracking processes.Considering the issue of implantation of this algorithm when dealing with real-time visual tracking,we improved set up of the Runtime Environment of OpenCV2.4.9 in VS2013,and we could operate and test traditional Mean Shift algorithm;according to those testing results,we provided improved solution and further compared the results both from traditional and targeted improvement which combined the advantages of kernel function calculation and Bhattacharyya Distance in real world.In this way,we could achieve the improvement of object size variation in complex environment.Finally,we achieved high accuracy of visual tracking in complex circumstance in this thesis,and kernel size adaptive geometry and achieved the high accurate and robust visual tracking based on the experimental data.At the same time,we achieved stable operation of the algorithm in OpenCV platform,provided solid foundation for further particle use.