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Cited by 1,742Video Surveillance and Tracking MethodsAdvanced Neural Network ApplicationsAutonomous Vehicle Technology and Safety

LaSOT: A High-Quality Benchmark for Large-Scale Single Object Tracking

Open access
Fan, Heng, Lin, Liting, Yang, Fan, Chu, Peng, Deng, Ge, Yu, Sijia, Bai, Hexin, Xu, Yong +2 more·2019·2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
DOI 10.1109/cvpr.2019.00552
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Abstract

In this paper, we present LaSOT, a high-quality benchmark for Large-scale Single Object Tracking. LaSOT consists of 1,400 sequences with more than 3.5M frames in total. Each frame in these sequences is carefully and manually annotated with a bounding box, making LaSOT the largest, to the best of our knowledge, densely annotated tracking benchmark. The average video length of LaSOT is more than 2,500 frames, and each sequence comprises various challenges deriving from the wild where target objects may disappear and re-appear again in the view. By releasing LaSOT, we expect to provide the community with a large-scale dedicated benchmark with high quality for both the training of deep trackers and the veritable evaluation of tracking algorithms. Moreover, considering the close connections of visual appearance and natural language, we enrich LaSOT by providing additional language specification, aiming at encouraging the exploration of natural linguistic feature for tracking. A thorough experimental evaluation of 35 tracking algorithms on LaSOT is presented with detailed analysis, and the results demonstrate that there is still a big room for improvements.

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    Lukezic, A., Vojir, T., Zajc, L. C., Matas, J., & Kristan, M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.515 · Cited by 1,153 · see citing works

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    Background

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    Background

  • The Visual Object Tracking VOT2015 Challenge Results

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    MethodSelf-citation

  • Encoding Color Information for Visual Tracking: Algorithms and Benchmark

    Liang, P., Blasch, E., & Ling, H. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2482905 · Cited by 726 · cited 6× · see citing works

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    BackgroundInfluentialSelf-citation

  • A survey of appearance models in visual object tracking

    Li, X., Hu, W., Shen, C., Zhang, Z., Dick, A., & Hengel, A. V. D. (2013) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2508037.2508039 · Cited by 705 · cited 2× · see citing works

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    Background

  • Fast Visual Tracking via Dense Spatio-temporal Context Learning

    Zhang, K., Lei Zhang, A., Liu, Q., Zhang, D. D., & Yang, M. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10602-1_9 · Cited by 681 · cited 4× · see citing works

    “We evaluate 35 algorithms on LaSOT to provide extensive baselines, comprising deep trackers ( e.g ., MDNet [42], TRACA [5], CFNet [50], SiamFC [4], StructSiam [59], DSiam [16], SINT [49] and VITAL [48]), correlation fil-ter trackers with hand-crafted features ( e.g ., ECO HC [7], DSST [8], CN [11], CSK [19], KCF [20],…”
    BackgroundInfluential

  • Context-Aware Correlation Filter Tracking

    Mueller, M. F., Smith, N., & Ghanem, B. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.152 · Cited by 662 · cited 3× · see citing works

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    Background

  • A Novel Performance Evaluation Methodology for Single-Target Trackers

    Kristan, M., Matas, J., Leonardis, A., Vojir, T., Pflugfelder, R. P., Fernandez, G., Nebehay, G., Porikli, F., & Zajc, L. Č. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2516982 · Cited by 613 · cited 6× · see citing works

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    BackgroundInfluential

  • The Visual Object Tracking VOT2014 Challenge Results

    Kristan, M., Pflugfelder, R. P., Leonardis, A., Matas, J., Zajc, L. Č., Nebehay, G., Vojíř, T., Fernández, G., Lukežič, A., Dimitriev, A., et al. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16181-5_14 · Cited by 606 · cited 3× · see citing works

    “Summaries of existing tracking benchmarks with highquality dense (per frame) annotations, including OTB-2013 [52], OTB-2015 [53], TC-128 [35], NUS-PRO [28], UAV123 [39], UAV20L [39], VOT-2014 [26], VOT-2017 [27] and LaSOT.”

  • VITAL: VIsual Tracking via Adversarial Learning

    Song, Y., Ma, C., Wu, X., Gong, L., Bao, L., Zuo, W., Shen, C., Lau, R. W. H., & Yang, M. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00937 · Cited by 595 · cited 5× · see citing works

    “Both MDNet and VITAL are trained in an online fashion, resulting in expensive computation and slow running speeds.”
    BackgroundInfluential

  • YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video

    Real, E., Shlens, J., Mazzocchi, S., Pan, X., & Vanhoucke, V. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.789 · Cited by 580 · cited 3× · see citing works

    “TrackingNet [41] is a subset of the video object detection benchmark YT-BB [43] by selecting 30K videos, each of which is annotated by a tracker.”
    BackgroundMethod

  • Person Search with Natural Language Description

    Li, S., Xiao, T., Li, H., Zhou, B., Yue, D., & Wang, X. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.551 · Cited by 571 · cited 1× · see citing works

    “, [21, 31]) including visual tracking [34].”

  • Need for Speed: A Benchmark for Higher Frame Rate Object Tracking

    Galoogahi, H. K., Fagg, A., Huang, C., Ramanan, D., & Lucey, S. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.128 · Cited by 542 · cited 4× · see citing works

    “NfS [14] provides 100 sequences with a high framerate of 240 fps, aiming to analyze the effects of appearance variations on tracking performance.”
    BackgroundInfluential

  • Natural Language Object Retrieval

    Hu, R., Xu, H., Rohrbach, M., Feng, J., Saenko, K., & Darrell, T. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.493 · Cited by 534 · cited 1× · see citing works

    “2) Different from existing datasets, LaSOT provides both visual bounding box annotations and rich natural language specification, which has recently been proven to be beneficial for various vision tasks ( e.g ., [21, 31]) including visual tracking [34].”
    Background

  • Deep visual tracking: Review and experimental comparison

    Li, P., Wang, D., Wang, L., & Lu, H. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.11.007 · Cited by 517 · cited 1× · see citing works

    “In this section, we mainly focus on the tracking benchmarks that are relevant to our work, and refer the readers to surveys [32, 47, 54, 30] for tracking algorithms.”
    Background

  • Parallel Tracking and Verifying: A Framework for Real-Time and High Accuracy Visual Tracking

    Fan, H., & Ling, H. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.585 · Cited by 330 · cited 2× · see citing works

    “…ECO [7]) and regularization techniques ( e.g ., BACF [15], SRDCF [10], CSRDCF [36], Staple CA [40] and STRCF [29]), ensemble trackers ( e.g ., PTAV [13], LCT [38], MEEM [56] and TLD [24]), sparse trackers ( e.g ., L1APG [2] and ASLA [23]), other representatives ( e.g ., CT [58], IVT [44], MIL [1]…”
    BackgroundSelf-citation

  • The Visual Object Tracking VOT2017 Challenge Results

    Kristan, M., Leonardis, A., Matas, J., Felsberg, M., Pflugfelder, R., Zajc, L. Č., Vojír, T., Häger, G., Lukežič, A., Eldesokey, A., et al. (2017) · 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw.2017.230 · Cited by 319 · cited 3× · see citing works

    “VOT-2017 [27] 60 41 356 293 1,500 21K 11.”

  • Structured Siamese Network for Real-Time Visual Tracking

    Zhang, Y., Wang, L., Qi, J., Wang, D., Feng, M., & Lu, H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01240-3_22 · Cited by 272 · cited 3× · see citing works

    “…35 algorithms on LaSOT to provide extensive baselines, comprising deep trackers ( e.g ., MDNet [42], TRACA [5], CFNet [50], SiamFC [4], StructSiam [59], DSiam [16], SINT [49] and VITAL [48]), correlation fil-ter trackers with hand-crafted features ( e.g ., ECO HC [7], DSST [8], CN [11], CSK [19],…”
    Background

  • Context-Aware Deep Feature Compression for High-Speed Visual Tracking

    Choi, J., Chang, H. J., Fischer, T., Yun, S., Lee, K., Jeong, J., Demiris, Y., & Choi, J. Y. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00057 · Cited by 238 · cited 3× · see citing works

    “, MDNet [42], TRACA [5], CFNet [50], SiamFC [4], StructSiam [59], DSiam [16], SINT [49] and VITAL [48]), correlation filter trackers with hand-crafted features (e.”
    Background

  • Visual Tracking Using Attention-Modulated Disintegration and Integration

    Choi, J., Chang, H. J., Jeong, J., Demiris, Y., & Choi, J. Y. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.468 · Cited by 191 · cited 2× · see citing works

    “…correlation fil-ter trackers with hand-crafted features ( e.g ., ECO HC [7], DSST [8], CN [11], CSK [19], KCF [20], fDSST [9], SAMF [33], SCT4 [6], STC [57] and Staple [3]) or deep features ( e.g ., HCFT [37] and ECO [7]) and regularization techniques ( e.g ., BACF [15], SRDCF [10], CSRDCF…”
    Background

  • Tracking by Natural Language Specification

    Li, Z., Ran, T., Gavves, E., Snoek, C. G. M., & Smeulders, A. W. M. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.777 · Cited by 184 · cited 1× · see citing works

    “2) Different from existing datasets, LaSOT provides both visual bounding box annotations and rich natural language specification, which has recently been proven to be beneficial for various vision tasks ( e.g ., [21, 31]) including visual tracking [34].”
    Background

  • NUS-PRO: A New Visual Tracking Challenge

    Li, A., Lin, M., Wu, Y., Yang, M., & Yan, S. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2417577 · Cited by 174 · cited 7× · see citing works

    “Summaries of existing tracking benchmarks with highquality dense (per frame) annotations, including OTB-2013 [52], OTB-2015 [53], TC-128 [35], NUS-PRO [28], UAV123 [39], UAV20L [39], VOT-2014 [26], VOT-2017 [27] and LaSOT.”
    BackgroundInfluential

  • Long-Term Tracking in the Wild: A Benchmark

    Valmadre, J., Bertinetto, L., Henriques, J. F., Ran, T., Vedaldi, A., Smeulders, A. W. M., Torr, P. H. S., & Gavves, E. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01219-9_41 · Cited by 157 · cited 8× · see citing works

    “Unlike TrackingNet, OxUvA is focused on long-term tracking.”
    BackgroundMethodInfluential

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Related

Authors
Fan, H., Lin, L., Yang, F., Chu, P., Deng, G., Yu, S., Bai, H., Xu, Y., Liao, C., & Ling, H.
Affiliations
  • Temple University (US)
  • South China University of Technology (CN)
  • Meituan (China) (CN)
ORCID
Venue
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
IEEE
Published
2019-06
Pages
5369-5378
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr.2019.00552
OpenAlex
W2891033863
Cited by
1,742
References
63
Citations by year
2026: 171 · 2025: 297 · 2024: 330 · 2023: 295 · 2022: 234 · 2021: 220 · 2020: 148 · 2019: 46 · 2018: 1
FWCI
52.22
Type
conference paper
Language
en
Open access
Licence
Locations
  • IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Science Foundation (1350521, 1449860, 1618398)
  • National Natural Science Foundation of China (61672241, 2016YFB1001200, U1611461, 1449860)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
Keywords
single object tracking, tracking benchmark, LaSOT, target occlusion, tracking algorithm evaluation

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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