Fast compressive tracking with robust example selection based on multiple instance learning in smart and autonomous systems (Withdrawn Publication. See vol. 76, pg. 761, 2018)

In Smart and Autonomous Systems (SAS) with a discriminative tracking system, the general way to choose the training examples is to sample the image patches surrounding the tracking object at the current frame and label them manually. However, when the tracking result is not quite accurate, the classifier updates with misaligned examples that bring unexpected noise, which could result in drift. We propose a method of example selection based on multiple instance learning (MIL) using an iterative strategy. The idea is to remove the worst example that has little contributions to the likelihood of bags in the positive bag for each round. The proposed system is built on FCT, a novel tracking algorithm that adopts compressive sensing for image representation. The qualitative and quantitative experimental results show the advantage of the proposed tracker in efficiency and accuracy to current state-of-the-art trackers when objects suffer from deformation and rotation. (C) 2017 Elsevier Ltd. All rights reserved.