Learning to adversarially blur visual object tracking

• Main Authors: GUO, Qing, CHENG, Ziyi, JUEFEI-XU, Felix, MA, Lei, XIE, Xiaofei, LIU, Yang, ZHAO, Jianjun
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Graphics and Human Computer Interfaces; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/7107; https://ink.library.smu.edu.sg/context/sis_research/article/8110/viewcontent/Guo_Learning_To_Adversarially_Blur_Visual_Object_Tracking_ICCV_2021_paper.pdf
• Institution: Singapore Management University

Motion blur caused by the moving of the object or camera during the exposure can be a key challenge for visual object tracking, affecting tracking accuracy significantly. In this work, we explore the robustness of visual object trackers against motion blur from a new angle, i.e., adversarial blur at...