Multi-layer convolutional network-based visual tracking via important region selection

The convolutional network-based tracking (CNT) algorithm provides a training network with warped target regions in the first frame instead of large auxiliary datasets, which solves the problem of convolutional neural network (CNN)-based tracking requiring very long training time and a large number of auxiliary training samples. However, the two-layer CNT uses only gray feature that causes sensitivity to appearance variations. Besides, some samples with useless information should be removed to avoid drifting problems. For these reasons, a multi-layer convolutional network-based visual tracking algorithm via important region selection (IRST) is proposed in this paper. The proposed important region selection model is built via high entropy selection and background discrimination, which enables the training samples to be informative in order to provide enough stable information and also be discriminative so as to resist distractors. The feature maps are also obtained by weighting the template filters with cluster weights. Instead of simple gray features, IRST adds the Gabor layer to explore the texture feature of the target that is effective on coping with illumination and rotation variations. Extensive experiments show that the proposed algorithm achieves superior performances in many challenging visual tracking tasks.