Visual Object Tracking Algorithm Based on Foreground Optimization

The introduction of object segmentation technology into the tracking field is a current research hotspot. At present, the tracking algorithm based on segmentation often calculates the minimum bounding rectangle as the bounding box according to the segmentation result. However, the complex target movement makes the bounding box contain more background, which leads to a decrease in accuracy. In response to the problem, this paper proposes a visual object tracking algorithm based on foreground optimization, which unifies the optimization of the scale and angle in the bounding box into the foreground optimization frame. First, the foreground ratio in the bounding box is evaluated. If it is less than the set threshold, the scale and angle of the bounding box are optimized; in the scale optimization module, the conditional probability of the bounding box is calculated in combination with the regression box, and the scale is optimized according to the results of the conditional probability; in the angle optimization module, many deviation angles are set for the bounding box, and the optimal bounding box angle is chosen by the foreground IoU (Intersection over Union) maximum strategy. The proposed method is applied to the SiamMask algorithm. Results show that the accuracy is improved by about 3.2%, 3.7% and 3.6% in the VOT2016, VOT2018 and VOT2019 data sets, respectively, while EAO is increased by about 1.8%, 1.9% and 1.6%, respectively. Moreover, our method has a certain universality for segmentation-based tracking algorithms. © 2022 Chinese Institute of Electronics. All rights reserved.