Visual Object Tracking Based on Multi-exemplar Regression Model

Most of the tracking-by-detection algorithms treat the tracking task as a category classification task, when the target experience deformation or encounter similar objects interference, the model drift is prone to occur. In this paper, a multi-exemplar regression tracking algorithm is proposed. In this algorithm, the exemplar model is considered to be more appropriate for tracking task, the exemplar model is set up by a frame image information, and the multi-exemplar model established in the time series can represent the target current state; in order to make the tracking algorithm adapt to the target deformation, the exemplar model is considered as the hidden variable by logistic regression model, together with the training sets from several recent frames sampling, can jointly build multi-exemplar regression tracking model. As the tracker builds multi-exemplar model on the whole, linking them together closely, it can effectively deal with the target deformation. Since the model drift only affects the exemplar model at current frame, each exemplar model is independent of each other, so the tracking algorithm can effectively reduce the influence of model drift on robust tracking. In the experiment, OTB 2013 benchmark and UAV 123 benchmark are used to verify the algorithm, DeepSRDCF, Siamese-fc and other algorithms act as the contrast algorithms, the experimental results show that the proposed tracker not only gives full play to the advantages of tracking based on multi-exemplar regression model, but also has good performance in deformation and background blur scene, and achieves three to five percent more than other advanced algorithms in the metrics of success rate and precision.