Papers › 1st Place Solution to ECCV-TAO-2020: Detect and Represent Any Object for Tracking
1st Place Solution to ECCV-TAO-2020: Detect and Represent Any Object for Tracking
Fei Du, Bo Xu, Jiasheng Tang, Yuqi Zhang, Fan Wang, Hao Li
We extend the classical tracking-by-detection paradigm to this tracking-any-object task. Solid detection results are first extracted from TAO dataset. Some state-of-the-art techniques like \textbf{BA}lanced-\textbf{G}roup \textbf{S}oftmax (\textbf{BAGS}\cite{li2020overcoming}) and DetectoRS\cite{qiao2020detectors} are integrated during detection. Then we learned appearance features to represent any object by training feature learning networks. We ensemble several models for improving detection and feature representation. Simple linking strategies with most similar appearance features and tracklet-level post association module are finally applied to generate final tracking results. Our method is submitted as \textbf{AOA} on the challenge website. Code is available at https://github.com/feiaxyt/Winner_ECCV20_TAO.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Multi-Object Tracking | TAO | AOA | AssocA | 30.56 | #9 of 9 | Archive leaderboard | report |
| Multi-Object Tracking | TAO | AOA | ClsA | 21.86 | #9 of 9 | Archive leaderboard | report |
| Multi-Object Tracking | TAO | AOA | LocA | 23.40 | #9 of 9 | Archive leaderboard | report |
| Multi-Object Tracking | TAO | AOA | TETA | 25.27 | #9 of 9 | Archive leaderboard | report |
| Multi-Object Tracking | TAO | AOA | Track mAP | 27.461 | #9 of 9 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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