构建首个大规模平面目标跟踪基准，推动机器人与AR应用发展。
PlanarTrack: A high-quality and challenging benchmark for large-scale planar object tracking
- 构建1150个序列、超73万帧的高精度平面跟踪数据集。
- 包含短时与长时视频，标注采用四角点人工精修。
- 适合研究真实场景下平面目标跟踪的算法优化者。
平面目标跟踪因在机器人和增强现实中的关键作用而受到越来越多关注。尽管近年来取得显著进展，但在深度学习时代，其发展仍远落后于通用目标跟踪，主要受限于缺乏大规模评估平台。为此，我们提出PlanarTrack，一个大规模、高质量且具有挑战性的平面跟踪基准。PlanarTrack包含1,150个序列，超过73.3万帧，涵盖1,000个短时序列和150个新长时视频，可全面评估短时与长时跟踪性能。所有视频均在非受控真实场景中采集，更具现实挑战性。为保证标注质量，每帧由四人多次审校并精细标注四角点。为提升目标多样性，每个序列仅包含一个独特目标，区别于现有基准。据我们所知，PlanarTrack是迄今最大、最多样、最具挑战性的平面跟踪专用数据集。为评估现有方法表现并为未来研究提供参照，我们对10种代表性平面追踪器进行了系统评测与深入分析。结果表明，顶级追踪器在该挑战性数据集上性能显著下降，说明仍需更多努力提升平面跟踪能力。数据与结果将公开于https://github.com/HengLan/PlanarTrack。
原文摘要 · Abstract (English)
Planar tracking has drawn increasing interest owing to its key roles in robotics and augmented reality. Despite recent great advancement, further development of planar tracking, particularly in the deep learning era, is largely limited compared to generic tracking due to the lack of large-scale platforms. To mitigate this, we propose PlanarTrack, a large-scale high-quality and challenging benchmark for planar tracking. Specifically, PlanarTrack consists of 1,150 sequences with over 733K frames, including 1,000 short-term and 150 new long-term videos, which enables comprehensive evaluation of short- and long-term tracking performance. All videos in PlanarTrack are recorded in unconstrained conditions from the wild, which makes PlanarTrack challenging but more realistic for real-world applications. To ensure high-quality annotations, each video frame is manually annotated by four corner points with multi-round meticulous inspection and refinement. To enhance target diversity of PlanarTrack, we only capture a unique target in one sequence, which is different from existing benchmarks. To our best knowledge, PlanarTrack is by far the largest and most diverse and challenging dataset dedicated to planar tracking. To understand performance of existing methods on PlanarTrack and to provide a comparison for future research, we evaluate 10 representative planar trackers with extensive comparison and in-depth analysis. Our evaluation reveals that, unsurprisingly, the top planar trackers heavily degrade on the challenging PlanarTrack, which indicates more efforts are required for improving planar tracking. Our data and results will be released at https://github.com/HengLan/PlanarTrack
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。