Fuzzy Detection Aided Real-Time and Robust Visual Tracking Under Complex Environments

Today, a new generation of artificial intelligence has brought several new research domains such as computer vision (CV). Thus, target tracking, the base of CV, has been a hotspot research domain. Correlation filter (CF)-based algorithm has been the basis of real-time tracking algorithms because of the high tracking efficiency. However, CF-based algorithms usually failed to track objects in complex environments. Therefore, this article proposes a fuzzy detection strategy to prejudge the tracking result. If the prejudge process determines that the tracking result is not good enough in the current frame, the stored target template is used for following tracking to avoid the template pollution. During testing on the OTB100 dataset, the experimental results show that the proposed auxiliary detection strategy improves the tracking robustness under complex environment by ensuring the tracking speed.

Automated summary

This article introduces a target tracking method based on a fuzzy detection strategy to improve tracking performance in complex environments. By pre-judging tracking results and taking corresponding measures, this method can effectively avoid template pollution, thus enhancing tracking robustness.