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Cited by 382Video Surveillance and Tracking MethodsAdvanced Neural Network ApplicationsVisual Attention and Saliency Detection

Learning Dynamic Memory Networks for Object Tracking

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Yang, Tianyu, Chan, Antoni B.·2018·Lecture Notes in Computer Science
DOI 10.1007/978-3-030-01240-3_10
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  • Previous Siamese trackers only exploit spatial information for tracking, while online methods use historical predictions for model updates.

    “Previous Siamese trackers [25, 51, 14, 6] only exploit the spatial information for tracking, while online methods [54, 57, 9, 3] use historical predictions for model updates.”

    From Learning Spatio-Temporal Transformer for Visual Tracking (2021) · cites this paper as [54]

    The Introduction (p. 1) says tracking-by-detection methods update their models using samples from subsequent frames based on newly predicted bounding boxes. But the claim that previous Siamese trackers use only spatial information is too broad: Related Work (p. 3) describes a tracker using historical target information for model updates and another Siamese tracker using optical flow. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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Flagged first, then by citations

Research works printed but not linked: 31

  1. [2]2. Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer Normalization. arXiv (2016)
  2. [3]3. Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., Torr, P.: Staple: Complementary Learners for Real-Time Tracking. In: CVPR (2016)
  3. [4]4. Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., Torr, P.H.S.: FullyConvolutional Siamese Networks for Object Tracking. In: ECCV Workshop on Visual Object Challenge (2016)
  4. [5]5. Choi, J., Chang, H.J., Yun, S., Fischer, T., Demiris, Y., Jin Young Choi: Attentional Correlation Filter Network for Adaptive Visual Tracking. In: CVPR (2017)
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  8. [9]9. Danelljan, M., H¨ ager, G., Khan, F.S., Felsberg, M.: Adaptive Decontamination of the Training Set: A Unified Formulation for Discriminative Visual Tracking. In: CVPR (2016)
  9. [10]10. Danelljan, M., Robinson, A., Khan, F.S., Felsberg, M.: Beyond correlation filters: Learning continuous convolution operators for visual tracking. In: ECCV (2016)
  10. [11]11. Graves, A., Wayne, G., Danihelka, I.: Neural Turing Machines. Arxiv (2014)
  11. [13]13. Guo, Q., Feng, W., Zhou, C., Huang, R., Wan, L., Wang, S.: Learning Dynamic Siamese Network for Visual Object Tracking. In: ICCV (2017)
  12. [16]16. Huang, C., Lucey, S., Ramanan, D.: Learning Policies for Adaptive Tracking with Deep Feature Cascades. In: ICCV (2017)
  13. [18]18. Kristan, M., Leonardis, A., Matas, J., Felsberg, M.: The Visual Object Tracking VOT2016 Challenge Results. In: ECCV Workshop on Visual Object Challenge (2016)
  14. [19]19. Lukeˇ ziˇ c, A., Voj´ ı, T., ˇ Cehovin, L., Matas, J., Kristan, M.: Discriminative Correlation Filter with Channel and Spatial Reliability. In: CVPR (2017)
  15. [20]20. Ma, C., Huang, J.b., Yang, X., Yang, M.h.: Hierarchical Convolutional Features for Visual Tracking. In: ICCV (2015)
  16. [21]21. Nam, H., Baek, M., Han, B.: Modeling and Propagating CNNs in a Tree Structure for Visual Tracking. In: ECCV (2016)
  17. [22]22. Nam, H., Han, B.: Learning Multi-Domain Convolutional Neural Networks for Visual Tracking. In: CVPR (2016)
  18. [23]23. Qi, Y., Zhang, S., Qin, L., Yao, H., Huang, Q., Yang, J.L.M.h.: Hedged Deep Tracking. In: CVPR (2016)
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  20. [25]25. Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., Lillicrap, T.: One-shot Learning with Memory-Augmented Neural Networks. In: ICML (2016)
  21. [26]26. Song, Y., Ma, C., Gong, L., Zhang, J., Lau, R., Yang, M.H.: CREST: Convolutional Residual Learning for Visual Tracking. In: ICCV (2017)
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  25. [30]30. Valmadre, J., Bertinetto, L., Henriques, F., Vedaldi, A., Torr, P.H.S.: End-to-end representation learning for Correlation Filter based tracking. In: CVPR (2017)
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  29. [36]36. Yang, T., Chan, A.B.: Recurrent Filter Learning for Visual Tracking. In: ICCV Workshop on Visual Object Challenge (2017)
  30. [37]37. Zeiler, M.D., Fergus, R.: Visualizing and Understanding Convolutional Networks. In: ECCV (2014)
  31. [38]38. Zhang, T., Xu, C., Yang, M.H.: Multi-task Correlation Particle Filter for Robust Object Tracking. In: CVPR (2017)

Cited by

Sorted by citations

Authors
Yang, T., & Chan, A. B.
Affiliations
City University of Hong Kong (HK)
Corresponding author
Yang, Tianyu
ORCID
Venue
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Published
2018
Pages
153-169
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-030-01240-3_10
OpenAlex
W2963471260
Cited by
382
References
7
Citations by year
2026: 15 · 2025: 39 · 2024: 54 · 2023: 62 · 2022: 60 · 2021: 55 · 2020: 59 · 2019: 36 · 2018: 2
FWCI
11.45
Type
conference paper
Language
en
Open access
Licence
Locations
  • Lecture notes in computer science (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • Nvidia (K40 GPU, Tesla K40 GPU, Tesla K40)
  • City University of Hong Kong (T32-101/15-R, 7004887)
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 › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
Keywords
object tracking, dynamic memory networks, template matching, appearance variation, attention mechanism, external memory, real-time tracking, visual object tracking

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