Skip to main content
SpringerLink
Log in

TFAtrack: Temporal Feature Aggregation for UAV Tracking and a Unified Benchmark

  • Conference paper
  • First Online:
Pattern Recognition and Computer Vision (PRCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13534))

Included in the following conference series:

  • 2786 Accesses

Abstract

Research on object detection and tracking has achieved remarkable progress in recent years. Due to the superior viewing angle and maneuverability advantages of unmanned aerial vehicles (UAVs), the application of UAV-based tracking is also undergoing rapid development. But since the targets captured by UAVs are tiny and all have similarities and low recognition, this leads to the great challenge of multiple-object tracking (MOT). To solve the two problems mentioned above, We propose TFATracking, a comprehensive framework that fully exploits temporal context for UAV tracking. To further reflect the effectiveness of the algorithm and promote the development of UAV object tracking, we present a large-scale, high-diversity benchmark for short-term UAV multi-object tracking named T2UAV in this work. It contains 20 UAV-captured video sequences with a total number of frames over 12k and an average video length of over 600 frames. We conduct a comprehensive performance evaluation of 8 MOT algorithms on the dataset and present a detailed analysis. We will release the dataset for free academic use.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Wojke, N., et al.: Simple online and realtime tracking with a deep association metric. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 3645–3649 (2017)

    Google Scholar 

  2. Yu, F., et al.: POI: Multiple Object Tracking with High Performance Detection and Appearance Feature (2016)

    Google Scholar 

  3. Dicle, C., et al.: The way they move: tracking multiple targets with similar appearance. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2304–2311 (2013)

    Google Scholar 

  4. Bewley, A., et al.: Simple online and realtime tracking. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 3464–3468 (2016)

    Google Scholar 

  5. Zhou, X., Koltun, V., Krähenbühl, P.: Tracking objects as points. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12349, pp. 474–490. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58548-8_28

    Chapter  Google Scholar 

  6. Ha, Q., et al.: MFNet: towards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes. In: International Conference on Intelligent Robots and Systems (IROS), pp. 5108–5115 (2017)

    Google Scholar 

  7. He, K., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  8. Ge, Z., et al.: Yolox: exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430pp (2021)

  9. Duan, K., et al.: Centernet: keypoint triplets for object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2019)

    Google Scholar 

  10. Redmon, J., et al.: Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)

  11. Lin, T.-Y., et al.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)

    Google Scholar 

  12. Lu, Z., et al.: Retinatrack: online single stage joint detection and tracking. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14668–14678 (2020)

    Google Scholar 

  13. Wang, Z., Zheng, L., Liu, Y., Li, Y., Wang, S.: Towards real-time multi-object tracking. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12356, pp. 107–122. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58621-8_7

    Chapter  Google Scholar 

  14. Law, H., et al.: Cornernet: detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 734–750 (2018)

    Google Scholar 

  15. Woo, S., et al.: Fairmot: on the fairness of detection and re-identification in multiple object tracking. Int. J. Comput. Vis. 1–19 (2021)

    Google Scholar 

  16. Yu, F., et al.: Deep layer aggregation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2403–2412 (2018)

    Google Scholar 

  17. Liang, C., et al.: One More Check: Making “Fake Background” Be Tracked Again. arXiv preprint arXiv:2104.09441 (2021)

  18. Chen, L., et al.: Real-time multiple people tracking with deeply learned candidate selection and person re-identification. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2018)

    Google Scholar 

  19. Chu, P., et al.: Transmot: spatial-temporal graph transformer for multiple object tracking. arXiv preprint arXiv:2104.00194 (2021)

  20. Wang, N., et al.: Transformer meets tracker: exploiting temporal context for robust visual tracking. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1571–1580 (2021)

    Google Scholar 

  21. Peng, J., et al.: Chained-tracker: chaining paired attentive regression results for end-to-end joint multiple-object detection and tracking. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12349, pp. 145–161. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58548-8_9

    Chapter  Google Scholar 

  22. Zhu, X., et al.: Flow-guided feature aggregation for video object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 408–417 (2017)

    Google Scholar 

  23. Hu, J., et al.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)

    Google Scholar 

  24. Park, J., et al.: Bam: bottleneck attention module. arXiv preprint arXiv:1807.06514 (2018)

  25. Woo, S., et al.: Cbam: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018)

    Google Scholar 

  26. Zhang, Y., et al.: A simple baseline for multi-object tracking. arXiv preprint arXiv:2004.01888 (2020)

  27. Shuai, B., et al.: SiamMOT: siamese multi-object tracking. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 12372–12382 (2021)

    Google Scholar 

  28. Milan, A., et al.: MOT16: A Benchmark for Multi-Object Tracking (2016)

    Google Scholar 

  29. Zeng, F., et al.: MOTR: End-to-End Multiple-Object Tracking with TRansformer. arXiv preprint arXiv:2105.03247 (2021)

  30. Liang, C., et al.: Rethinking the competition between detection and ReID in multi-object tracking. arXiv preprint arXiv:2010.12138 (2020)

  31. Bernardin, K.: Evaluating multiple object tracking performance: the clear mot metrics. EURASIP J. Image Video Process. 1–10 (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, School of Information and Computer, Anhui Agricultural University, Hefei, China

    Xiaowei Zhao & Youhua Zhang

Authors
  1. Xiaowei Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Youhua Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Youhua Zhang .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Shiqi Yu

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zhaoxiang Zhang

  3. Hong Kong Baptist University, Hong Kong, China

    Pong C. Yuen

  4. Northwestern Polytechnical University, Xi’an, China

    Junwei Han

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Hong Kong Baptist University, Hong Kong, China

    Yike Guo

  7. Sun Yat-sen University, Guangzhou, China

    Jianhuang Lai

  8. Southern University of Science and Technology, Shenzhen, China

    Jianguo Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhao, X., Zhang, Y. (2022). TFAtrack: Temporal Feature Aggregation for UAV Tracking and a Unified Benchmark. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13534. Springer, Cham. https://doi.org/10.1007/978-3-031-18907-4_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-18907-4_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-18906-7

  • Online ISBN: 978-3-031-18907-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation