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Backbone is All Your Need: A Simplified Architecture for Visual Object Tracking

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Computer Vision – ECCV 2022 (ECCV 2022)

Abstract

Exploiting a general-purpose neural architecture to replace hand-wired designs or inductive biases has recently drawn extensive interest. However, existing tracking approaches rely on customized sub-modules and need prior knowledge for architecture selection, hindering the development of tracking in a more general system. This paper presents a Simplified Tracking architecture (SimTrack) by leveraging a transformer backbone for joint feature extraction and interaction. Unlike existing Siamese trackers, we serialize the input images and concatenate them directly before the one-branch backbone. Feature interaction in the backbone helps to remove well-designed interaction modules and produce a more efficient and effective framework. To reduce the information loss from down-sampling in vision transformers, we further propose a foveal window strategy, providing more diverse input patches with acceptable computational costs. Our SimTrack improves the baseline with 2.5%/2.6% AUC gains on LaSOT/TNL2K and gets results competitive with other specialized tracking algorithms without bells and whistles. The source codes are available at https://github.com/LPXTT/SimTrack.

B. Chen and P. Li—Equal contribution.

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References

  1. Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., Torr, P.H.S.: Fully-convolutional Siamese networks for object tracking. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 850–865. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48881-3_56

    Chapter  Google Scholar 

  2. Bhat, G., Danelljan, M., Gool, L.V., Timofte, R.: Learning discriminative model prediction for tracking. In: ICCV (2019)

    Google Scholar 

  3. Bromley, J., Guyon, I., Lecun, Y., Säckinger, E., Shah, R.: Signature verification using a Siamese time delay neural network. In: NeurIPS, pp. 737–744 (1993)

    Google Scholar 

  4. Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213–229. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_13

    Chapter  Google Scholar 

  5. Chen, B., et al.: PSVIT: better vision transformer via token pooling and attention sharing. arXiv preprint arXiv:2108.03428 (2021)

  6. Chen, B., et al.: GLIT: neural architecture search for global and local image transformer. In: ICCV (2021)

    Google Scholar 

  7. Chen, B., Wang, D., Li, P., Wang, S., Lu, H.: Real-time ‘actor-critic’ tracking. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 328–345. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_20

    Chapter  Google Scholar 

  8. Chen, T., Saxena, S., Li, L., Fleet, D.J., Hinton, G.: Pix2seq: a language modeling framework for object detection. arXiv preprint arXiv:2109.10852 (2021)

  9. Chen, X., Yan, B., Zhu, J., Wang, D., Yang, X., Lu, H.: Transformer tracking. In: CVPR (2021)

    Google Scholar 

  10. Chen, X., Xie, S., He, K.: An empirical study of training self-supervised vision transformers. In: ICCV (2021)

    Google Scholar 

  11. Chen, Z., Zhong, B., Li, G., Zhang, S., Ji, R.: Siamese box adaptive network for visual tracking. In: CVPR (2020)

    Google Scholar 

  12. Choi, J., Kwon, J., Lee, K.M.: Deep meta learning for real-time visual tracking based on target-specific feature space. CoRR abs/1712.09153 (2017)

    Google Scholar 

  13. Danelljan, M., Bhat, G., Khan, F.S., Felsberg, M.: ATOM: accurate tracking by overlap maximization. In: CVPR (2019)

    Google Scholar 

  14. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

  15. Dosovitskiy, A., et al.: An image is worth 16\(\times \)16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)

  16. Guo, D., Shao, Y., Cui, Y., Wang, Z., Zhang, L., Shen, C.: Graph attention tracking. In: CVPR (2021)

    Google Scholar 

  17. Guo, M., et al.: Learning target-aware representation for visual tracking via informative interactions. arXiv preprint arXiv:2201.02526 (2022)

  18. Guo, Q., Feng, W., Zhou, C., Huang, R., Wan, L., Wang, S.: Learning dynamic Siamese network for visual object tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1763–1771 (2017)

    Google Scholar 

  19. He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: CVPR (2022)

    Google Scholar 

  20. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: ICCV (2017)

    Google Scholar 

  21. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)

    Google Scholar 

  22. Huang, L., Zhao, X., Huang, K.: Got-10k: a large high-diversity benchmark for generic object tracking in the wild. CoRR abs/1810.11981 (2018)

    Google Scholar 

  23. Jaegle, A., et al.: Perceiver IO: a general architecture for structured inputs & outputs. arXiv preprint arXiv:2107.14795 (2021)

  24. Kamath, A., Singh, M., LeCun, Y., Synnaeve, G., Misra, I., Carion, N.: Mdetr-modulated detection for end-to-end multi-modal understanding. In: ICCV (2021)

    Google Scholar 

  25. Laina, I., Rupprecht, C., Belagiannis, V., Tombari, F., Navab, N.: Deeper depth prediction with fully convolutional residual networks. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 239–248. IEEE (2016)

    Google Scholar 

  26. Li, B., Wu, W., Wang, Q., Zhang, F., Xing, J., Yan, J.: SiamRPN++: evolution of Siamese visual tracking with very deep networks. In: CVPR (2019)

    Google Scholar 

  27. Li, B., Yan, J., Wu, W., Zhu, Z., Hu, X.: High performance visual tracking with Siamese region proposal network. In: CVPR (2018)

    Google Scholar 

  28. Li, P., Chen, B., Ouyang, W., Wang, D., Yang, X., Lu, H.: GradNet: gradient-guided network for visual object tracking. In: ICCV (2019)

    Google Scholar 

  29. Li, P., Wang, D., Wang, L., Lu, H.: Deep visual tracking: review and experimental comparison. Pattern Recogn. 76, 323–338 (2018)

    Article  Google Scholar 

  30. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  31. Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: hierarchical vision transformer using shifted windows. In: ICCV (2021)

    Google Scholar 

  32. Mayer, C., Danelljan, M., Paudel, D.P., Van Gool, L.: Learning target candidate association to keep track of what not to track. In: ICCV (2021)

    Google Scholar 

  33. Mu, N., Kirillov, A., Wagner, D., Xie, S.: Slip: self-supervision meets language-image pre-training. arXiv preprint arXiv:2112.12750 (2021)

  34. Mueller, M., Smith, N., Ghanem, B.: A benchmark and simulator for UAV tracking. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 445–461. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_27

    Chapter  Google Scholar 

  35. Müller, M., Bibi, A., Giancola, S., Alsubaihi, S., Ghanem, B.: TrackingNet: a large-scale dataset and benchmark for object tracking in the wild. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11205, pp. 310–327. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01246-5_19

    Chapter  Google Scholar 

  36. Radford, A., et al.: Learning transferable visual models from natural language supervision. In: ICML (2021)

    Google Scholar 

  37. Radford, A., Narasimhan, K., Salimans, T., Sutskever, I.: Improving language understanding by generative pre-training (2018)

    Google Scholar 

  38. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. Adv. Neural. Inf. Process. Syst. 28, 1–9 (2015)

    Google Scholar 

  39. Shen, Q., et al.: Unsupervised learning of accurate Siamese tracking. In: CVPR (2022)

    Google Scholar 

  40. Tang, S., Chen, D., Bai, L., Liu, K., Ge, Y., Ouyang, W.: Mutual CRF-GNN for few-shot learning. In: CVPR (2021)

    Google Scholar 

  41. Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., Jégou, H.: Training data-efficient image transformers & distillation through attention. In: ICML (2021)

    Google Scholar 

  42. Vaswani, A., et al.: Attention is all you need. In: NeurIPS, vol. 30 (2017)

    Google Scholar 

  43. Wang, L., Zhang, J., Wang, O., Lin, Z., Lu, H.: SDC-depth: semantic divide-and-conquer network for monocular depth estimation. In: CVPR (2020)

    Google Scholar 

  44. Wang, N., Zhou, W., Wang, J., Li, H.: Transformer meets tracker: exploiting temporal context for robust visual tracking. In: CVPR (2021)

    Google Scholar 

  45. Wang, N., Zhou, W., Wang, J., Li, H.: Transformer meets tracker: exploiting temporal context for robust visual tracking. In: ICCV (2021)

    Google Scholar 

  46. Wang, Q., Zhang, L., Bertinetto, L., Hu, W., Torr, P.H.S.: Fast online object tracking and segmentation: a unifying approach. In: CVPR (2019)

    Google Scholar 

  47. Wang, W., et al.: Pyramid vision transformer: a versatile backbone for dense prediction without convolutions. In: ICCV (2021)

    Google Scholar 

  48. Wang, X., et al.: Towards more flexible and accurate object tracking with natural language: algorithms and benchmark. In: CVPR (2021)

    Google Scholar 

  49. Wang, Y., et al.: Revisiting the transferability of supervised pretraining: an MLP perspective. In: CVPR (2022)

    Google Scholar 

  50. Xu, Y., Wang, Z., Li, Z., Ye, Y., Yu, G.: SiamFC++: towards robust and accurate visual tracking with target estimation guidelines. In: AAAI (2020)

    Google Scholar 

  51. Yan, B., Peng, H., Fu, J., Wang, D., Lu, H.: Learning spatio-temporal transformer for visual tracking. arXiv preprint arXiv:2103.17154 (2021)

  52. Yu, Y., Xiong, Y., Huang, W., Scott, M.R.: Deformable Siamese attention networks for visual object tracking. In: CVPR (2020)

    Google Scholar 

  53. Zhang, Z., et al.: Joint task-recursive learning for semantic segmentation and depth estimation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11214, pp. 238–255. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01249-6_15

    Chapter  Google Scholar 

  54. Zhang, Z., Liu, Y., Wang, X., Li, B., Hu, W.: Learn to match: automatic matching network design for visual tracking. In: ICCV (2021)

    Google Scholar 

  55. Zhang, Z., Peng, H., Fu, J., Li, B., Hu, W.: Ocean: object-aware anchor-free tracking. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12366, pp. 771–787. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58589-1_46

    Chapter  Google Scholar 

  56. Zheng, S., et al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: CVPR (2021)

    Google Scholar 

  57. Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable DETR: deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159 (2020)

  58. Zhu, X., et al.: Uni-perceiver: pre-training unified architecture for generic perception for zero-shot and few-shot tasks. arXiv preprint arXiv:2112.01522 (2021)

  59. Zhu, Z., et al.: Distractor-aware Siamese networks for visual object tracking. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11213, pp. 103–119. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01240-3_7

    Chapter  Google Scholar 

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Acknowledgements

This work was supported by the Australian Research Council Grant DP200103223, Australian Medical Research Future Fund MRFAI000085, CRC-P Smart Material Recovery Facility (SMRF) - Curby Soft Plastics, and CRC-P ARIA - Bionic Visual-Spatial Prosthesis for the Blind.

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Correspondence to Lei Bai .

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Chen, B. et al. (2022). Backbone is All Your Need: A Simplified Architecture for Visual Object Tracking. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13682. Springer, Cham. https://doi.org/10.1007/978-3-031-20047-2_22

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  • DOI: https://doi.org/10.1007/978-3-031-20047-2_22

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