{"url":"/dataset/rgbt234","name":"RGBT234","full_name":null,"description_markdown":"The RGBT234 dataset is a comprehensive video dataset specifically designed for RGB-T (Red-Green-Blue and Thermal) tracking purposes. This dataset addresses the limitations of existing datasets like OSU-CT, LITIV, and GTOT in terms of size. RGBT234 consists of 234 RGB-T videos, each containing both an RGB video and a thermal video. The total number of frames in the dataset is approximately 234,000, with the largest video pair containing up to 8,000 frames.Each frame in the RGBT234 dataset is annotated with a minimum bounding box that covers the target for both the RGB and thermal modalities. The dataset also includes various environmental challenges such as rainy conditions, nighttime scenes, cold and hot weather scenarios. To analyze the performance of different tracking algorithms based on specific attributes, the RGBT234 dataset annotates 12 attributes and provides baseline trackers, including both deep learning and non-deep learning methods like structured SVM, sparse representation, and correlation filter-based trackers. Additionally, the dataset employs 5 metrics to evaluate the performance of RGB-T trackers effectively.","description_withheld":null,"homepage":"https://sites.google.com/view/ahutracking001/","introduced_date":"2018-05-23","introduced_date_note":null,"introduced_by":{"paper":"/paper/rgb-t-object-trackingbenchmark-and-baseline","title":"RGB-T Object Tracking:Benchmark and Baseline","first_author":"Chenglong Li","url":null},"license":null,"modalities":[{"name":"Videos","url":"/datasets/modality/videos"},{"name":"Tracking","url":"/datasets/modality/tracking"}],"tasks":[{"name":"Rgb-T Tracking","url":"/task/rgb-t-tracking","datasets_with_task":"/datasets/task/rgb-t-tracking"}],"languages":[],"variants":["RGBT234"],"data_loaders":[],"num_papers_in_archive":38,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/rgb-t-tracking-on-rgbt234","task":"Rgb-T Tracking","dataset_variant":"RGBT234","rows":42,"metrics":["Precision","Success"],"first_row_in_archive_order":{"model":"SUTrack-L224","paper":"/paper/sutrack-towards-simple-and-unified-single","metrics":{"Precision":"94.6","Success":"70.8"},"code_links":[{"title":"chenxin-dlut/sutrack","url":"https://github.com/chenxin-dlut/sutrack"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/breaking-shallow-limits-task-driven-pixel","title":"Breaking Shallow Limits: Task-Driven Pixel Fusion for Gap-free RGBT Tracking","date":"2025-03-14","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/adaptive-perception-for-unified-visual-multi","title":"Adaptive Perception for Unified Visual Multi-modal Object Tracking","date":"2025-02-10","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/sutrack-towards-simple-and-unified-single","title":"SUTrack: Towards Simple and Unified Single Object Tracking","date":"2024-12-26","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/exploiting-multimodal-spatial-temporal","title":"Exploiting Multimodal Spatial-temporal Patterns for Video Object Tracking","date":"2024-12-20","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":3,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/breaking-modality-gap-in-rgbt-tracking","title":"Breaking Modality Gap in RGBT Tracking: Coupled Knowledge Distillation","date":"2024-10-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/cross-fusion-rgb-t-tracking-with-bi","title":"Cross Fusion RGB-T Tracking with Bi-directional Adapter","date":"2024-08-30","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/rgbt-tracking-via-all-layer-multimodal","title":"RGBT Tracking via All-layer Multimodal Interactions with Progressive Fusion Mamba","date":"2024-08-16","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/mambavt-spatio-temporal-contextual-modeling","title":"MambaVT: Spatio-Temporal Contextual Modeling for robust RGB-T Tracking","date":"2024-08-15","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/2408-02222","title":"Cross-modulated Attention Transformer for RGBT Tracking","date":"2024-08-05","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/transformer-based-rgb-t-tracking-with-channel","title":"Transformer-based RGB-T Tracking with Channel and Spatial Feature Fusion","date":"2024-05-06","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/after-attention-based-fusion-router-for-rgbt","title":"AFter: Attention-based Fusion Router for RGBT Tracking","date":"2024-05-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/revisiting-rgbt-tracking-benchmarks-from-the","title":"Revisiting RGBT Tracking Benchmarks from the Perspective of Modality Validity: A New Benchmark, Problem, and Method","date":"2024-04-30","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/middle-fusion-and-multi-stage-multi-form","title":"Middle Fusion and Multi-Stage, Multi-Form Prompts for Robust RGB-T Tracking","date":"2024-03-27","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/from-two-stream-to-one-stream-efficient-rgb-t","title":"From Two-Stream to One-Stream: Efficient RGB-T Tracking via Mutual Prompt Learning and Knowledge Distillation","date":"2024-03-25","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/sdstrack-self-distillation-symmetric-adapter","title":"SDSTrack: Self-Distillation Symmetric Adapter Learning for Multi-Modal Visual Object Tracking","date":"2024-03-24","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/onetracker-unifying-visual-object-tracking","title":"OneTracker: Unifying Visual Object Tracking with Foundation Models and Efficient Tuning","date":"2024-03-14","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/transformer-rgbt-tracking-with-spatio","title":"Transformer RGBT Tracking with Spatio-Temporal Multimodal Tokens","date":"2024-01-03","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/temporal-adaptive-rgbt-tracking-with-modality","title":"Temporal Adaptive RGBT Tracking with Modality Prompt","date":"2024-01-02","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/bi-directional-adapter-for-multi-modal","title":"Bi-directional Adapter for Multi-modal Tracking","date":"2023-12-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/single-model-and-any-modality-for-video","title":"Single-Model and Any-Modality for Video Object Tracking","date":"2023-11-27","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/generative-based-fusion-mechanism-for-multi","title":"Generative-based Fusion Mechanism for Multi-Modal Tracking","date":"2023-09-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/rgb-t-tracking-via-multi-modal-mutual-prompt","title":"RGB-T Tracking via Multi-Modal Mutual Prompt Learning","date":"2023-08-31","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/unified-single-stage-transformer-network-for","title":"Unified Single-Stage Transformer Network for Efficient RGB-T Tracking","date":"2023-08-26","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/seqtrack-sequence-to-sequence-learning-for","title":"Unified Sequence-to-Sequence Learning for Single- and Multi-Modal Visual Object Tracking","date":"2023-04-27","rows_on_this_dataset":4,"code_links":1,"syntology":null},{"paper":"/paper/visual-prompt-multi-modal-tracking","title":"Visual Prompt Multi-Modal Tracking","date":"2023-03-20","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/efficient-rgb-t-tracking-via-cross-modality","title":"Efficient RGB-T Tracking via Cross-Modality Distillation","date":"2023-01-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/bridging-search-region-interaction-with","title":"Bridging Search Region Interaction With Template for RGB-T Tracking","date":"2023-01-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/prompting-for-multi-modal-tracking","title":"Prompting for Multi-Modal Tracking","date":"2022-07-29","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/visible-thermal-uav-tracking-a-large-scale","title":"Visible-Thermal UAV Tracking: A Large-Scale Benchmark and New Baseline","date":"2022-04-08","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/attribute-based-progressive-fusion-network","title":"Attribute-Based Progressive Fusion Network for RGBT Tracking","date":"2022-01-26","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/dynamic-fusion-network-for-rgbt-tracking","title":"Dynamic Fusion Network for RGBT Tracking","date":"2021-09-16","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/mfgnet-dynamic-modality-aware-filter","title":"MFGNet: Dynamic Modality-Aware Filter Generation for RGB-T Tracking","date":"2021-07-22","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/rgbt-tracking-via-multi-adapter-network-with","title":"RGBT Tracking via Multi-Adapter Network with Hierarchical Divergence Loss","date":"2020-11-14","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/duality-gated-mutual-condition-network-for","title":"Duality-Gated Mutual Condition Network for RGBT Tracking","date":"2020-11-14","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/jointly-modeling-motion-and-appearance-cues","title":"Jointly Modeling Motion and Appearance Cues for Robust RGB-T Tracking","date":"2020-07-04","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/cross-modal-pattern-propagation-for-rgb-t","title":"Cross-Modal Pattern-Propagation for RGB-T Tracking","date":"2020-06-01","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":10,"samples_ran":5,"samples_unverified":5,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}