Y ROBUST ONLINE MULTI-OBJECT TRACKING BASED ON KCF TRACKERS AND REASSIGNMENT

There is a big challenge in online multi-object tracking-by-detection, which caused by frequent occlusions, false alarms or miss detections and other factors. In this paper, we proposed an improved fast online multi-object tracking method through taking into account the results of multiple single-object trackers and detections synthetically. To solve the fixed scale problem of conventional kernelized correlation filter in single-object tracker we used, trackers are associated with detections based on position and size and then an adaptive mechanism of trackers is established. In addition, in order to correctly reassign detections to lost trackers after occlusion, we propose to attach occluded object to occluders to predict its position. And then, an association strategy on the basis of appearance, position, attached position and size reliably reassigns detections to re-appearing objects. Experiments on public datasets demonstrate that our proposed method performs favorably against the state-of-the-art methods.