An online learning target tracking method based on extreme learning machine
Liyan Xie, Yuanlong Yu, Zhiyong Huang · 2016
Target tracking is one of the important tasks in computer vision. It aims to detect and track one or more particular objects in videos. The target and background may change in the process of tracking. In order to solve this problem, this paper proposes an online learning target tracking method based on extreme learning machine (ELM). First of all, we capture the target and background regions in the first few frames of video and extract the histograms of oriented gradients (HOG) features of regions into ELM. Secondly, using the method of sliding window to detect the candidate region after loading a new image. Finally, according to the tracking result, the classifier can be updated for online learning. In order to promote the detection speed, this method predicts a region in the current frame according to the target position of the previous frame. The predicted region is called the candidate region. Experiment results have shown that this proposed method not only achieves high accuracy but also can adapt to the changes of target and background.