arXiv:2102.06733
공식 코드 · Papers with Code
Revisiting the details when evaluating a visual tracker
원문 초록
Visual tracking algorithms are naturally adopted in various applications, there have been several benchmarks and many tracking algorithms, more expected to appear in the future. In this report, I focus on single object tracking and revisit the details of tracker evaluation based on widely used OTB\cite{otb} benchmark by introducing a simpler, accurate, and extensible method for tracker evaluation and comparison. Experimental results suggest that there may not be an absolute winner among tracking algorithms. We have to perform detailed analysis to select suitable trackers for use cases.
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R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning
Adversarial Attack for RGB-Event based Visual Object Tracking
Less is More: Token Context-aware Learning for Object Tracking
Exploring Enhanced Contextual Information for Video-Level Object Tracking
A Distractor-Aware Memory for Visual Object Tracking with SAM2
SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory
토론
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