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Recently, deep learning has achieved very promising results in visual objecttracking. Deep neural networks in existing tracking methods require a lot oftraining data to learn a large number of parameters. However, training data isnot sufficient for visual object tracking as annotations of a target object areonly available in the first frame of a test sequence. In this paper, we proposeto learn hierarchical features for visual object tracking by using treestructure based Recursive Neural Networks (RNN), which have fewer parametersthan other deep neural networks, e.g. Convolutional Neural Networks (CNN).First, we learn RNN parameters to discriminate between the target object andbackground in the first frame of a test sequence. Tree structure over localpatches of an exemplar region is randomly generated by using a bottom-up greedysearch strategy. Given the learned RNN parameters, we create two dictionariesregarding target regions and corresponding local patches based on the learnedhierarchical features from both top and leaf nodes of multiple random trees. Ineach of the subsequent frames, we conduct sparse dictionary coding on allcandidates to select the best candidate as the new target location. Inaddition, we online update two dictionaries to handle appearance changes oftarget objects. Experimental results demonstrate that our feature learningalgorithm can significantly improve tracking performance on benchmark datasets.

Learning Hierarchical Features for Visual Object Tracking with Recursive Neural Networks