Abstract
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.
Highlights
Car sharing services provide customers access to shared vehicles for short-term use. ey can reduce inner-city traffic, trip cost, congestion, and environmental pollution and have developed rapidly in recent years
How to update the tracking model online based on the analysis of current tracking performance is still an open problem. is study tries to bridge this research gap, and the main contributions are follows: (1) Introduce an object-specific intersection over union (IOU) predictor which trained offline on a large number of image pairs to estimate the performance of current tracking result for object model updating
Visual object trackers can acquire the trajectories of the objects such as pedestrians and vehicles in traffic scene and make the car sharing services more secure and efficient
Summary
Car sharing services provide customers access to shared vehicles for short-term use. ey can reduce inner-city traffic, trip cost, congestion, and environmental pollution and have developed rapidly in recent years. A typical visual object tracking method consists of five components, namely, feature extraction, motion model, appearance model, model updating, and integration process [3]. Most object trackers use the simplest linear weighting for model updating, in which a new appearance model is obtained by weighting the old one and the tracking result of the current frame. If the tracking result of the current frame is reliable and the object is not occluded, a small weight factor of current tracking result may cause the appearance model not to be updated adequately. (1) Introduce an object-specific IOU predictor which trained offline on a large number of image pairs to estimate the performance of current tracking result for object model updating.
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