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.
翻译:虽然为评价最新算法制定了若干基准,但缺乏在野生环境中而不是在有限的实验室环境中捕捉到的视频序列。在本文件中,我们提出了一个精心设计的平面物体跟踪基准,其中包含在自然环境中抽样的30个平面物体的210个视频。特别是,我们为每个物体拍摄了7个视频,涉及各种挑战性因素,即规模变化、旋转、观点扭曲、运动模糊、隔离、视野外和不受限制。地面真相是经过仔细说明的半人工的,以确保质量。此外,11个最先进的平面物体跟踪方法使用两个评价指标对基准进行了评价,并为评价结果提供了详细分析。我们期望拟议的基准将有利于今后对平面物体跟踪的研究。