Visual object tracking based on adaptive deblurring integrating motion blur perception

Abstract

Visual object tracking in motion-blurred scenes is crucial for applications such as traffic monitoring and navigation, including intelligent video surveillance, robotic vision navigation, and automated driving. Existing tracking algorithms primarily cater to sharp images, exhibiting significant performance degradation in motion-blurred scenes. Image degradation and decreased contrast resulting from motion blur compromise feature extraction quality. This paper proposes a visual object tracking algorithm, SiamADP, based on adaptive deblurring and integrating motion blur perception. First, the proposed algorithm employs a blur perception mechanism to detect whether the input image is severely blurred. After that, an effective motion blur removal network is used to generate blur-free images, facilitating rich and useful feature information extraction. Given the scarcity of motion blur datasets for object tracking evaluation, four test datasets are proposed: three synthetic datasets and a manually collected and labeled real motion blur dataset. Comparative experiments with existing trackers demonstrate the effectiveness and robustness of SiamADP in motion blur scenarios, validating its performance.