Visual Object Tracking with Online Updating for Car Sharing Services

摘要

In this paper, we address the problem of online updating of visual object tracker for car sharing services. The key idea is to adjust the updating rate adaptively according to the tracking performance of the current frame. Instead of setting a fixed weight for all the frames in the updating of the object model, we assign the current frame a larger weight if its corresponding tracking result is relatively accurate and unbroken and a smaller weight on the contrary. To implement it, the current estimated bounding box's intersection over union (IOU) is calculated by an IOU predictor which is trained offline on a large number of image pairs and used as a guidance to adjust the updating weights online. Finally, we imbed the proposed model update strategy in a lightweight baseline tracker. Experiment results on both traffic and nontraffic datasets verify that though the error of predicted IOU is inevitable, the proposed method can still improve the accuracy of object tracking compared with the baseline object tracker.