为快速移动物体追踪构建新数据集并验证高效模型性能
Benchmarking EfficientTAM on FMO datasets
- 构建含物体尺寸信息的FMOX JSON元数据文件
- EfficientTAM在FMOX上表现接近专用追踪算法
- 开源数据与代码助力后续研究
快速移动物体（FMO）的实时轻量级追踪仍是计算机视觉中的挑战。本文首次为四个开源FMO图像序列数据集引入配套的JSON元数据文件，并扩展其标注信息，新增物体尺寸等真值数据，形成名为FMOX的结构化数据格式。利用FMOX，我们测试了近期提出的通用追踪基础模型EfficientTAM，结果表明其性能可媲美专为这些数据集设计的先进追踪流程。对比基于轨迹交并比（TIoU）指标，结果展示该模型在复杂运动场景下的鲁棒性。相关代码与FMOX数据均开源，便于其他机器学习系统用于处理FMO数据。
原文摘要 · Abstract (English)
Fast and tiny object tracking remains a challenge in computer vision and in this paper we first introduce a JSON metadata file associated with four open source datasets of Fast Moving Objects (FMOs) image sequences. In addition, we extend the description of the FMOs datasets with additional ground truth information in JSON format (called FMOX) with object size information. Finally we use our FMOX file to test a recently proposed foundational model for tracking (called EfficientTAM) showing that its performance compares well with the pipelines originally taylored for these FMO datasets. Our comparison of these state-of-the-art techniques on FMOX is provided with Trajectory Intersection of Union (TIoU) scores. The code and JSON is shared open source allowing FMOX to be accessible and usable for other machine learning pipelines aiming to process FMO datasets.
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