Accurate Bounding-box Regression with Distance-IoU Loss for Visual Tracking

TL;DR

This paper introduces a novel distance-IoU loss for visual tracking that improves bounding-box accuracy by considering center point distance, resulting in more precise target estimation while maintaining real-time speed.

Contribution

The paper proposes a new DIoU loss for bounding-box regression in visual tracking, enhancing accuracy over IoU loss and integrating an online classification strategy for real-time performance.

Findings

Achieves competitive accuracy compared to state-of-the-art trackers.

Maintains real-time tracking speed.

Improves bounding-box estimation precision.

Abstract

Most existing trackers are based on using a classifier and multi-scale estimation to estimate the target state. Consequently, and as expected, trackers have become more stable while tracking accuracy has stagnated. While trackers adopt a maximum overlap method based on an intersection-over-union (IoU) loss to mitigate this problem, there are defects in the IoU loss itself, that make it impossible to continue to optimize the objective function when a given bounding box is completely contained within/without another bounding box; this makes it very challenging to accurately estimate the target state. Accordingly, in this paper, we address the above-mentioned problem by proposing a novel tracking method based on a distance-IoU (DIoU) loss, such that the proposed tracker consists of target estimation and target classification. The target estimation part is trained to predict the DIoU score between the target ground-truth bounding-box and the estimated bounding-box. The DIoU loss can maintain the advantage provided by the IoU loss while minimizing the distance between the center points of two bounding boxes, thereby making the target estimation more accurate. Moreover, we introduce a classification part that is trained online and optimized with a Conjugate-Gradient-based strategy to guarantee real-time tracking speed. Comprehensive experimental results demonstrate that the proposed method achieves competitive tracking accuracy when compared to state-of-the-art trackers while with a real-time tracking speed.