Model uncertainty guides visual object tracking

• Main Authors: ZHOU, Lijun, LEDENT, Antoine, HU, Qintao, LIU, Ting, ZHANG, Jianlin, KLOFT, Marius
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Object Tracking; Computer Vision; Machine Learning.; Artificial Intelligence and Robotics; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/7204; https://ink.library.smu.edu.sg/context/sis_research/article/8207/viewcontent/16473_Article_Text_19967_1_2_20210518.pdf
• Institution: Singapore Management University

Model object trackers largely rely on the online learning of a discriminative classifier from potentially diverse sample frames. However, noisy or insufficient amounts of samples can deteriorate the classifiers' performance and cause tracking drift. Furthermore, alterations such as occlusion an...