Precise Regression for Bounding Box Correction for Improved Tracking Based on Deep Reinforcement Learning

Abstract

In this paper, we propose a precise regression approach for correcting imprecise bounding box using deep reinforcement learning. Object tracking task essentially builds trajectory of a moving object based on detection and tracking algorithms and its current state is indicated by having the object encapsulated with a bounding box corresponding to its position and size. However due to the imperfect detection and tracking algorithms operating in complex scene, it is difficult to obtain the precise bounding box as errors frequently occur producing oversized, partial, and false bounding box, respectively. To correct the error, we train an intelligent agent that move the bounding box to the right position and scale it to its correct size matching to that of the true target. The agent is trained by deep Q-Iearning and evaluated on several state-of-the-art multiple object tracking approaches. The experimental results demonstrate that our proposed framework can effectively eliminate the object tracking bounding box error and its robustness is verified by realizing improved tracking performance in complex scene.