Browse State-of-the-Art › Thermal Infrared Object Tracking
Thermal Infrared Object Tracking
8 papers with code · 0 benchmarks · 2 datasets archive 2025-07-28
Benchmarks archive 2025-07-28
No benchmark for this task in the archive.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
2 datasets whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
No subtask under this task in the archive's task tree.
Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
8 shown of 8 papers with code (12 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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24 Nov 2022 1 repository listed Syntology ran 0 of 1 samples · 1 unverifiedThe evaluation of object detection models is usually performed by optimizing a single metric, e.
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3 Aug 2020 1 repository listedWe evaluate and analyze more than 30 trackers on LSOTB-TIR to provide a series of baselines, and the results show that deep trackers achieve promising performance.
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26 Nov 2019 1 repository listedThese two feature models are learned using a multi-task matching framework and are jointly optimized on the TIR tracking task.
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9 Jun 2019 1 repository listedThese two similarities complement each other and hence enhance the discriminative capacity of the network for handling distractors.
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6 Mar 2018 1 repository listedWe evaluated the performance of the system by training it to recognise 32 material types in both indoor and outdoor environments.
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18 Jan 2018 1 repository listedThe ability to evaluate the TIR pedestrian tracker fairly, on a benchmark dataset, is significant for the development of this field.
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27 Nov 2017 1 repository listedIn this paper, we cast the TIR tracking problem as a similarity verification task, which is coupled well to the objective of the tracking task.
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15 Oct 2017 1 repository listedWe observe that the features from the fully-connected layer are not suitable for thermal infrared tracking due to the lack of spatial information of the target, while the features from the convolution layers are.
Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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