Research Of Visual Tracking Algorithm Based On Deep Reinforcement Learning

Visual tracking plays an important role in the field of computer vision and attach more attention by researchers in recent years.Visual tracking find the location of tracked object which is labeled in the first frame and annotated by human power.In recent years,the convolutional neural networks get breakthroughs in the field of visual tracking.However,which is only used to extract local image feature.The relationship between these frames in the ignored in current researches.In this thesis,we proposed a visual tracking method based on policy gradient method which utilized Convolutional Neural Networks to map the image to its corresponding actions and Long Short Time Memory to learn the relationship between history actions and current actions.Reinforcement learning is used to construct a Markov decision process to learn a map between states and actions.When introduced the reinforcement learning to visual tracking,we can apply MDP to visual tracking method: the current object is transformed by some actions to others state.The recurrent neural network utilized the current input and the previous hidden state to learn the map between history information and the current state.In this thesis we introduced the Convolutional Neural Networks based on deep learning and Policy gradient based on reinforcement learning.We also introduced visual tracking methods based on those algorithm.In this basement,we proposed a visual tracking method based on reinforcement learning and experiments demonstrate that our developed method could applied to real life and improved tracking efficiency.