Object-Aware Adaptive Convolution Kernel Attention Mechanism in Siamese Network for Visual Tracking
Source abstract
Abstract
As a classic framework for visual object tracking, the Siamese convolutional neural network has received widespread attention from the research community. This method uses a convolutional neural network to obtain the object features and to match them with the search area features to achieve object tracking. In this work, we observe that the contribution of each convolution kernel in the convolutional neural network for object tracking tasks is different. We propose an object-aware convolution kernel attention mechanism. Based on the characteristics of each object, the convolution kernel features are dynamically weighted to improve the expression ability of object features. The experiments performed using OTB and VOT benchmark datasets show that the performance of the tracking method fused with the convolution kernel attention mechanism is significantly better compared with the original method. Moreover, the attention mechanism can also be integrated with other tracking frameworks as an independent module to improve the performance.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Video Surveillance and Tracking Methods · Impact of Light on Environment and Health · Fire Detection and Safety Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, China
Chinesisch-Deutsche Technische Fakultat, Qingdao University of Science and Technology, Qingdao 266061, China
Faculty of Electrical Engineering and Computer Science, University of Kassel, 34001 Kassel, Germany
College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China
Funding
Tai Shan Scholar Foundation · Shandong Province Key Research and Development Plan · National Natural Science Foundation of China
References (39)
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1. Chopra, S., Hadsell, R., and LeCun, Y. (2005, January 20–25). Learning a Similarity Metric Discriminatively with Application to Face Verification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, CA, USA.
2. Bertinetto, L., Valmadre, J., Henriques, J.O.F., Vedaldi, A., and Torr, P.H.S. (2016, January 8–16). Fully-Convolutional Siamese Networks for Object Tracking. Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands.10.1007/978-3-319-48881-3_56 ↗
3. Valmadre, J., Bertinetto, L., Henriques, J.O.F., Vedaldi, A., and Torr, P.H.S. (2017, January 21–26). End-to-end representation learning for Correlation Filter based tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.10.1109/CVPR.2017.531 ↗
4. Tao, R., Gavves, E., and Smeulders, A.W. (2016, January 27–30). Siamese instance search for tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2016.158 ↗
5. Wang, Q., Teng, Z., Xing, J., Gao, J., Hu, W., and Maybank, S. (2018, January 18–23). Learning attentions: Residual attentional Siamese Network for high performance online visual tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00510 ↗
6. He, A., Luo, C., Tian, X., and Zeng, W. (2018, January 18–23). A Twofold Siamese Network for Real-Time Object Tracking. Proceedings of the The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.10.1109/CVPR.2018.00508 ↗
7. Gong, K., Cao, Z., Xiao, Y., and Fang, Z. (2018, January 9–11). Online Update Siamese Network for Unmanned Surface Vehicle Tracking. Proceedings of the 11th International Conference of Intelligent Robotics and Applications, Newcastle, Australia.10.1007/978-3-319-97589-4_14 ↗
8. Li, B., Yan, J., Wu, W., Zhu, Z., and Hu, X. (2018, January 18–23). High Performance Visual Tracking With Siamese Region Proposal Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00935 ↗
9. Faster R-CNN: Towards real-time object detection with region proposal networks
10. Zhu, Z., Wang, Q., Li, B., Wu, W., Yan, J., and Hu, W. (2018, January 8–14). Distractor-aware Siamese networks for visual object tracking. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-01240-3_7 ↗
11. Li, B., Wu, W., Wang, Q., Zhang, F., Xing, J., and Yan, J. (2019, January 15–20). SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.10.1109/CVPR.2019.00441 ↗
12. Zhang, Z., Peng, H., and Wang, Q. (2019, January 15–20). Deeper and Wider Siamese Networks for Real-Time Visual Tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.10.1109/CVPR.2019.00472 ↗
13. Wang, N., Song, Y., Ma, C., Zhou, W., Liu, W., and Li, H. (2019, January 15–20). Unsupervised Deep Tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.10.1109/CVPR.2019.00140 ↗
14. SiamCAN: Real-time Visual Tracking based on Siamese Center-aware Network10.1109/TIP.2021.3060905 ↗
15. Inverted Residual Siamese Visual Tracking With Feature Crossing Network10.1109/ACCESS.2021.3056194 ↗
16. Zeng, Y., Zeng, B., Yin, X., and Chen, G. (2021). SiamPCF: Siamese point regression with coarse-fine classification network for visual tracking. Appl. Intell., 1–14.10.1007/s10489-021-02651-5 ↗
17. Multi-level prediction Siamese network for real-time UAV visual tracking10.1016/j.imavis.2020.104002 ↗
18. Ma, C., Huang, J.-B., Yang, X., and Yang, M.-H. (2015, January 7–13). Hierarchical convolutional features for visual tracking. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.10.1109/ICCV.2015.352 ↗
19. Hu, J., Shen, L., and Sun, G. (2018, January 18–23). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.10.1109/CVPR.2018.00745 ↗
20. Vedaldi, A., and Lenc, K. (2015, January 26–30). MatConvNet: Convolutional Neural Networks for MATLAB. Proceedings of the 23rd ACM international conference on Multimedia, Brisbane, Australia.10.1145/2733373.2807412 ↗
21. Wu, Y., Lim, J., and Yang, M.-H. (2013, January 23–28). Online Object Tracking: A Benchmark. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, OR, USA.10.1109/CVPR.2013.312 ↗
22. Object Tracking Benchmark10.1109/TPAMI.2014.2388226 ↗
23. Kristan, M., Matas, J., Leonardis, A., Felsberg, M., Pflugfelder, R., Kamarainen, J.-K., Cehovin Zajc, L., Drbohlav, O., Lukezic, A., and Berg, A. (2019, January 27–28). The seventh visual object tracking vot2019 challenge results. Proceedings of the IEEE International Conference on Computer Vision Workshops, Seoul, Korea.
24. High-Speed Tracking with Kernelized Correlation Filters
25. Struck: Structured Output Tracking with Kernels10.1109/TPAMI.2015.2509974 ↗
26. Tracking-Learning-Detection10.1109/TPAMI.2011.239 ↗
27. Dinh, T.B., Vo, N., and Medioni, G.E.R. (2011, January 20–25). Context tracker: Exploring supporters and distracters in unconstrained environments. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, USA.10.1109/CVPR.2011.5995733 ↗
28. Henriques, J.O.F., Caseiro, R., Martins, P., and Batista, J. (2012, January 7–13). Exploiting the Circulant Structure of Tracking-by-Detection with Kernels. Proceedings of the European Conference on Computer Vision (ECCV), Berlin, Heidelberg.10.1007/978-3-642-33765-9_50 ↗
29. Kwon, J., and Lee, K.M. (2010, January 13–18). Visual tracking decomposition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.10.1109/CVPR.2010.5539821 ↗
30. Kwon, J., and Lee, K.M. (2011, January 6–13). Tracking by sampling trackers. Proceedings of the International Conference on Computer Vision, Barcelona, Spain.
31. Liu, B., Huang, J., Yang, L., and Kulikowsk, C. (2011, January 20–25). Robust tracking using local sparse appearance model and k-selection. Proceedings of the IEEE Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, USA.10.1109/CVPR.2011.5995730 ↗
32. Grabner, H., Grabner, M., and Bischof, H. (2006, January 4–7). Real-time tracking via on-line boosting. Proceedings of the British Machine Vision Conference, Edinburgh, UK.10.5244/C.20.6 ↗
33. Danelljan, M., Robinson, A., Khan, F.S., and Felsberg, M. (2016, January 8–16). Beyond correlation filters: Learning continuous convolution operators for visual tracking. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.10.1007/978-3-319-46454-1_29 ↗
34. Lukezic, A., Vojir, T., Čehovin Zajc, L., Matas, J., and Kristan, M. (2017, January 21–26). Discriminative correlation filter with channel and spatial reliability. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.515 ↗
35. Danelljan, M., Bhat, G., Khan, F.S., and Felsberg, M. (2017, January 21–26). ECO: Efficient Convolution Operators for Tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.10.1109/CVPR.2017.733 ↗
36. Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., and Torr, P.H. (2016, January 27–30). Staple: Complementary learners for real-time tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.156 ↗
37. Bolme, D.S., Beveridge, J.R., Draper, B.A., and Lui, Y.M. (2010, January 13–18). Visual object tracking using adaptive correlation filters. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, USA.10.1109/CVPR.2010.5539960 ↗
38. Danelljan, M., Ger, G.H., Khan, F.S., and Felsberg, M. (2014, January 1–5). Accurate Scale Estimation for Robust Visual Tracking. Proceedings of the British Machine Vision Conference, Nottingham, UK.10.5244/C.28.65 ↗
39. Incremental learning for robust visual tracking10.1007/s11263-007-0075-7 ↗