Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning

Citations Over TimeTop 1% of 2017 papers

Abstract

This paper proposes a novel tracker which is controlled by sequentially pursuing actions learned by deep reinforcement learning. In contrast to the existing trackers using deep networks, the proposed tracker is designed to achieve a light computation as well as satisfactory tracking accuracy in both location and scale. The deep network to control actions is pre-trained using various training sequences and fine-tuned during tracking for online adaptation to target and background changes. The pre-training is done by utilizing deep reinforcement learning as well as supervised learning. The use of reinforcement learning enables even partially labeled data to be successfully utilized for semi-supervised learning. Through evaluation of the OTB dataset, the proposed tracker is validated to achieve a competitive performance that is three times faster than state-of-the-art, deep network-based trackers. The fast version of the proposed method, which operates in real-time on GPU, outperforms the state-of-the-art real-time trackers.

Related Papers

• → A Comprehensive Analysis of Machine Learning Techniques in Biomedical Image Processing Using Convolutional Neural Network(2022)18 cited
• → Efficient Disease Risk Prediction based on Deep Learning Approach(2022)10 cited
• → A Survey on Deep Learning Models Embed Bio-Inspired Algorithms in Cardiac Disease Classification(2022)4 cited
• → Skin Disease Diagnostic techniques using deep learning(2022)2 cited
• → Machine Learning and Deep Learning applications-a vision using the SPSS Method(2020)