Deep Reinforcement Learning for Visual Object Tracking in Videos
This work addresses the problem of accurate and efficient visual tracking for video analysis applications, presenting a novel integration of methods but is incremental in combining existing neural network components.
The paper tackles visual object tracking in videos by formulating it as a sequential decision-making process and introduces a fully end-to-end recurrent convolutional neural network agent trained with reinforcement learning, achieving state-of-the-art performance on an existing benchmark with faster-than-real-time frame-rates.
In this paper we introduce a fully end-to-end approach for visual tracking in videos that learns to predict the bounding box locations of a target object at every frame. An important insight is that the tracking problem can be considered as a sequential decision-making process and historical semantics encode highly relevant information for future decisions. Based on this intuition, we formulate our model as a recurrent convolutional neural network agent that interacts with a video overtime, and our model can be trained with reinforcement learning (RL) algorithms to learn good tracking policies that pay attention to continuous, inter-frame correlation and maximize tracking performance in the long run. The proposed tracking algorithm achieves state-of-the-art performance in an existing tracking benchmark and operates at frame-rates faster than real-time. To the best of our knowledge, our tracker is the first neural-network tracker that combines convolutional and recurrent networks with RL algorithms.