Object Tracking Based on Modified TLD Framework Using Compressive Sensing Features

Visual object tracking is widely researched but still challenging as both accuracy and efficiency must be considered in a single system. CT tracker can achieve a good real-time performance but is not very robust to fast movements. TLD framework has the ability to re-initialize object but can’t handle rotation and runs with low efficiency. In this paper, we propose a tracking algorithm combining the CT into TLD framework to overcome the disadvantages of each other. With the scale information obtained by an optical-flow tracker, we select samples for detector and use the detection result to correct the opticalflow tracker. The features are extracted using compressive sensing to improve the processing speed. The classifier parameters are updated by online learning. Considering the situation of continuous loss of object, a sliding window searching is also employed. Experiment results show that our proposed method achieves good performances in both precision and speed.