首页|Study Findings from Sun Yat-sen University Broaden Understanding of Robotics and Automation (E-gnn: an Enhanced Method for Multi-object Tracking With Collective Motion Patterns)
Study Findings from Sun Yat-sen University Broaden Understanding of Robotics and Automation (E-gnn: an Enhanced Method for Multi-object Tracking With Collective Motion Patterns)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Data detailed on Robotics - Robotics a nd Automation have been presented. Accordingto news reporting originating in Sh enzhen, People’s Republic of China, by NewsRx journalists, researchstated, “The long-term consistent visual tracking of large-scale moving swarms of animals or autonomousmoving robots (AMR) is extremely challenging when the three factors are involved: 1) similar appearanceof animals or AMR, 2) frequent and unpredict able occlusions, and 3) non-linear maneuvers. When facing such difficulties, exi sting multiple object tracking (MOT) algorithms are prone to identity switches a ndsuffer severe performance degredation.”
ShenzhenPeople’s Republic of ChinaAs iaRobotics and AutomationRoboticsSun Yat-sen University
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