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Feature tracking is the building block of many applications such as visualodometry, augmented reality, and target tracking. Unfortunately, thestate-of-the-art vision-based tracking algorithms fail in surgical images dueto the challenges imposed by the nature of such environments. In this paper, weproposed a novel and unified deep learning-based approach that can learn how totrack features reliably as well as learn how to detect such reliable featuresfor tracking purposes. The proposed network dubbed as Deep-PT, consists of atracker network which is a convolutional neural network simulatingcross-correlation in terms of deep learning and two fully connected networksthat operate on the output of intermediate layers of the tracker to detectfeatures and predict trackability of the detected points. The ability to detectfeatures based on the capabilities of the tracker distinguishes the proposedmethod from previous algorithms used in this area and improves the robustnessof the algorithms against dynamics of the scene. The network is trained usingmultiple datasets due to the lack of specialized dataset for feature trackingdatasets and extensive comparisons are conducted to compare the accuracy ofDeep-PT against recent pixel tracking algorithms. As the experiments suggest,the proposed deep architecture deliberately learns what to track and how totrack and outperforms the state-of-the-art methods.

Deep Feature Tracker: A Novel Application for Deep Convolutional Neural Networks