Planar Object Tracking in the Wild: A Benchmark
원문 초록
Planar object tracking is an actively studied problem in vision-based robotic applications. While several benchmarks have been constructed for evaluating state-of-the-art algorithms, there is a lack of video sequences captured in the wild rather than in constrained laboratory environment. In this paper, we present a carefully designed planar object tracking benchmark containing 210 videos of 30 planar objects sampled in the natural environment. In particular, for each object, we shoot seven videos involving various challenging factors, namely scale change, rotation, perspective distortion, motion blur, occlusion, out-of-view, and unconstrained. The ground truth is carefully annotated semi-manually to ensure the quality. Moreover, eleven state-of-the-art algorithms are evaluated on the benchmark using two evaluation metrics, with detailed analysis provided for the evaluation results. We expect the proposed benchmark to benefit future studies on planar object tracking.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
RGBTrack: Fast, Robust Depth-Free 6D Pose Estimation and Tracking
ReaMOT: A Benchmark and Framework for Reasoning-based Multi-Object Tracking
EOTNet: Deep Memory Aided Bayesian Filter for Extended Object Tracking
Asynchronous Multi-Object Tracking with an Event Camera
A Simple Detector with Frame Dynamics is a Strong Tracker
LLM-Empowered Embodied Agent for Memory-Augmented Task Planning in Household Robotics
토론
이 논문과 연결된 포럼 글이 아직 없습니다.