Steady object tracking based on online sample mining

Tracking methods based on Correlation Filter have been constantly improved in tracking accuracy and robustness. However, it still challenged in background clutter, rotation changes and occlusion, the drift of the model was one of the main reasons. In this paper, we propose an online sample training method based on Gaussian Mixture Model. The maximum response value, obtained from the convolution of samples and filters, is used to judge the availability of the online samples, which is able to reduce the interference of wrong online samples. Then, through Gaussian Mixture Model, samples are classified to strengthen the diversity of the sample set, which can avoid model drift effectively. Besides, we also propose a model update criterion to enhance the stability of the tracker, and heighten the efficiency of calculation. This criterion is determined by changes of target in scale and displacement. We perform comprehensive experiments on three benchmarks: OTB100, VOT2016 and VOT-TIR2016. Comparing with other trackers, our tracker has better robustness in the condition of background clutter, rotation change and occlusion. Moreover, its speed also maintains real-time performance.