  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.
