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Compact discriminative object representation via weakly supervised learning for real-time visual tracking

Object representations are of great importance for robust visual tracking. Although the high-dimensional representation can effectively encode the input data with more information, exploiting it in a real-time tracking system would be intractable and infeasible due to the high computational cost and memory requirements. In this study, the authors propose a compact discriminative object representation to achieve both good tracking accuracy and efficiency. An ensemble of weak training sets is generated based on the self-representative ability of tracking samples, which is applied to learn discriminative functions. Each candidate is represented by the concatenation of project values on all the weak training sets. Tracking is then carried out within a Bayesian inference framework where the classification score of the support vector machine is used to construct the observation model. The evaluations on TB50 benchmark dataset demonstrate that the proposed algorithm is much more computationally efficient than the state-of-the-art methods with comparable accuracy.