Real-time least-squares ensemble visual tracking

In this study, the authors present a novel ensemble tracking system by formulating the tracking task in terms of a linear regression which is a least-squares problem. A set of weak classifiers are trained using least squares which are solved efficiently using the Moore-Penrose inverse. Then, these weak classifiers are combined into a strong classifier using bagging. The strong classifier is used to recognise the target and locate its position, which is obtained efficiently in the Fourier domain. For obtaining a good ensemble, a novel sampling strategy is proposed to train accurate and diverse weak classifiers. By exploiting historical targets to monitor the training process, pose change and occlusion are well-handled. The proposed method is extensively evaluated using a variety of evaluation protocols on the recent standard datasets including OTB50, OTB100 and VOT2016. Experimental results show that the proposed methodology performs favourably against state-of-the-art methods in terms of efficiency, accuracy and robustness.