Papers › TrackFormer: Multi-Object Tracking with Transformers

TrackFormer: Multi-Object Tracking with Transformers

7 Jan 2021CVPR 2022 1arXiv:2101.02702archive 2025-07-28

Tim Meinhardt, Alexander Kirillov, Laura Leal-Taixe, Christoph Feichtenhofer

The challenging task of multi-object tracking (MOT) requires simultaneous reasoning about track initialization, identity, and spatio-temporal trajectories. We formulate this task as a frame-to-frame set prediction problem and introduce TrackFormer, an end-to-end trainable MOT approach based on an encoder-decoder Transformer architecture. Our model achieves data association between frames via attention by evolving a set of track predictions through a video sequence. The Transformer decoder initializes new tracks from static object queries and autoregressively follows existing tracks in space and time with the conceptually new and identity preserving track queries. Both query types benefit from self- and encoder-decoder attention on global frame-level features, thereby omitting any additional graph optimization or modeling of motion and/or appearance. TrackFormer introduces a new tracking-by-attention paradigm and while simple in its design is able to achieve state-of-the-art performance on the task of multi-object tracking (MOT17 and MOT20) and segmentation (MOTS20). The code is available at https://github.com/timmeinhardt/trackformer .

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Code

timmeinhardt/trackformer officialmentioned in papermentioned on GitHubpytorch report
acaelles97/devis mentioned on GitHubpytorchApache-2.0 report

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Tasks

DecoderMulti-Object TrackingObjectObject TrackingVideo Understanding

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Multi-Object Tracking MOT17 TrackFormer IDF1 68.0 #30 of 48 Archive leaderboard report
Multi-Object Tracking MOT17 TrackFormer MOTA 74.1 #30 of 48 Archive leaderboard report
Multi-Object Tracking MOT17 TrackFormer e2e-MOT Yes #30 of 48 Archive leaderboard report
Multi-Object Tracking MOTS20 TrackFormer sMOTSA 54.9 #4 of 6 Archive leaderboard report
Object Tracking QuadTrack TrackFormer HOTA 19.62 #5 of 8 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.

Methods

Absolute Position EncodingsAdamAttentionBPEConvolutionDense ConnectionsDetrDropoutFeedforward NetworkLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformer

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