WaspMOT：赤眼蜂长期多目标跟踪基准
WaspMOT: A Benchmark for Long-Term Multi-Object Tracking of Trichogramma Wasps
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中文总结 AI 辅助
研究针对多目标跟踪在长期身份保持评估不足的问题，引入WaspMOT基准，通过对赤眼蜂长时间跟踪构建数据集，评估五种检测跟踪方法，发现都有轨迹碎片化问题，简单拼接基线可提升性能，揭示了现有方法局限。
中文摘要 AI 辅助
多目标跟踪（MOT）在以短视频序列为主的基准测试中表现出色，但此类数据集无法充分评估长期身份保持情况。我们引入WaspMOT，这是一个通过在受控生态实验中对赤眼蜂进行长时间跟踪来解决这一差距的基准。该数据集包含10个序列，每个序列约12000帧，有密集的MOTChallenge注释和神谕检测以分离关联性能。与现有基准不同，WaspMOT形成封闭集跟踪场景。我们通过评估五种检测跟踪方法建立基准，结果表明所有方法都存在显著轨迹碎片化问题，一个简单的空间轨迹拼接基线持续提高了性能。WaspMOT为研究长期关联提供了新基准，揭示了当前跟踪方法在传统数据集上无法观察到的局限性。
英文摘要
Multi-object tracking (MOT) has achieved strong performance on benchmarks dominated by short video sequences. However, such datasets do not adequately evaluate long-term identity preservation, where objects must be tracked consistently over extended durations. We introduce WaspMOT, a benchmark designed to address this gap through long-duration tracking of Trichogramma wasps in controlled ecological experiments. The dataset contains 10 sequences of approximately 12,000 frames each (over 8 minutes at 25 FPS), with dense MOTChallenge annotations and oracle detections to isolate association performance. Unlike existing benchmarks, WaspMOT forms a closed-set tracking scenario where all individuals remain present throughout the sequence, requiring consistent identity assignment across thousands of frames despite abrupt jumps, occlusions, and highly similar appearance. We establish a benchmark by evaluating five tracking-by-detection methods, including ByteTrack, BoT-SORT, C-BIoU, OC-SORT, and McByte, under a unified protocol. Results show that all methods suffer from significant trajectory fragmentation, highlighting the difficulty of long-term identity preservation even with perfect detections. A simple spatial tracklet stitching baseline consistently improves performance, indicating that substantial gains remain possible. WaspMOT provides a new benchmark for studying long-term association and reveals limitations of current tracking approaches that are not observable on conventional datasets. The benchmark will be made publicly available at the project repository: https://github.com/tstanczyk95/WaspMOT/ .
发表机构
- Inria（法国国家信息与自动化技术研究院）
- INRAE Institut Sophia Agrobiotech（法国国家农业生物技术研究院）
- Université Côte d'Azur（蔚蓝海岸大学）
- Indian Institute of Technology Delhi（德里印度理工学院）
- Institute of Plant Protection, Chinese Academy of Agricultural Sciences（中国农业科学院植物保护研究所）
机构由 AI 辅助整理，请以论文原文为准。
