深層特徴トラッカー：深層畳み込みニューラルネットワークの新応用
Deep Feature Tracker: A Novel Application for Deep Convolutional Neural Networks
手術画像でも頑健に特徴点を追跡できるよう、追跡ネットワークと特徴検出・追跡可能性予測ネットワークを統合した深層学習手法Deep-PTを提案し、従来のピクセル追跡法を上回る精度を示した。
著者: Mostafa Parchami, Saif Iftekar Sayed
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
Feature tracking is the building block of many applications such as visual odometry, augmented reality, and target tracking. Unfortunately, the state-of-the-art vision-based tracking algorithms fail in surgical images due to the challenges imposed by the nature of such environments. In this paper, we proposed a novel and unified deep learning-based approach that can learn how to track features reliably as well as learn how to detect such reliable features for tracking purposes. The proposed network dubbed as Deep-PT, consists of a tracker network which is a convolutional neural network simulating cross-correlation in terms of deep learning and two fully connected networks that operate on the output of intermediate layers of the tracker to detect features and predict trackability of the detected points. The ability to detect features based on the capabilities of the tracker distinguishes the proposed method from previous algorithms used in this area and improves the robustness of the algorithms against dynamics of the scene. The network is trained using multiple datasets due to the lack of specialized dataset for feature tracking datasets and extensive comparisons are conducted to compare the accuracy of Deep-PT against recent pixel tracking algorithms. As the experiments suggest, the proposed deep architecture deliberately learns what to track and how to track and outperforms the state-of-the-art methods.