Papers › Efficient Visual Tracking with Exemplar Transformers

Efficient Visual Tracking with Exemplar Transformers

17 Dec 2021arXiv:2112.09686archive 2025-07-28

Philippe Blatter, Menelaos Kanakis, Martin Danelljan, Luc van Gool

The design of more complex and powerful neural network models has significantly advanced the state-of-the-art in visual object tracking. These advances can be attributed to deeper networks, or the introduction of new building blocks, such as transformers. However, in the pursuit of increased tracking performance, runtime is often hindered. Furthermore, efficient tracking architectures have received surprisingly little attention. In this paper, we introduce the Exemplar Transformer, a transformer module utilizing a single instance level attention layer for realtime visual object tracking. E.T.Track, our visual tracker that incorporates Exemplar Transformer modules, runs at 47 FPS on a CPU. This is up to 8x faster than other transformer-based models. When compared to lightweight trackers that can operate in realtime on standard CPUs, E.T.Track consistently outperforms all other methods on the LaSOT, OTB-100, NFS, TrackingNet, and VOT-ST2020 datasets. Code and models are available at https://github.com/pblatter/ettrack.

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Code

pblatter/ettrack officialmentioned in papermentioned on GitHubpytorch report
visionml/pytracking officialmentioned in papermentioned on GitHubpytorch report

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Tasks

Object TrackingVideo Object TrackingVisual Object TrackingVisual Tracking

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Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Video Object Tracking NT-VOT211 E.T.Tracker AUC 34.38 #24 of 43 Archive leaderboard report
Video Object Tracking NT-VOT211 E.T.Tracker Precision 46.98 #24 of 43 Archive leaderboard report

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Methods

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformer

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