Effective object tracking using extreme learning machine with smoothness and preference regularisation


A completely unique object tracking method is proposed that takes advantage of the fast learning capability of extreme learning machine (ELM). Specifically, object tracking is viewed as a binary classification downside, and ELM is utilised for locating the optimal separate hyperplane between the item and backgrounds efficiently. To realize a additional sturdy tracking, two constraints are introduced in ELM training: (i) target visual changes across frames are swish (i.e. smoothness) and (ii) probabilities to be true object of image samples round the tracked target trajectory are preferred than those of background ones (i.e. preference). Experiments on difficult sequences demonstrate that the proposed tracker performs favourably against the state-of-the-art ways.

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