Visual object tracking based on adaptive deblurring integrating motion blur perception

作     者：Sun, Lifan Gong, Baocheng Liu, Jianfeng Gao, Dan 

作者机构：Henan Univ Sci & Technol Sch Informat Engn Luoyang 471023 Peoples R China Longmen Lab Luoyang 471000 Peoples R China Henan Acad Sci Zhengzhou 450046 Peoples R China 

出 版 物：《JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION》 (J Visual Commun Image Represent)

年 卷 期：2025年第107卷

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China Young Backbone Teachers in Universities of Henan Province, China [2020GGJS073] Major Science and Technology Projects of Longmen Laboratory Key Research and Development and Promotion of Special (Science and Technology) Project of Henan Province, China Key Scientific Research Project of Higher Education Institutions in Henan Province, China [24B520010] Frontier Exploration Project of Longmen Laboratory, China [LMQYTSKT034] 

主　　题：Visual object tracking Adaptive deblurring Siamese network Motion blur perception 

摘      要：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.