دوره 22، شماره 1 - ( 3-1404 )                   جلد 22 شماره 1 صفحات 112-83 | برگشت به فهرست نسخه ها


XML English Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Bayat M H, Tarvirdizadeh B, Shahbazi M. A Review of Vision-Based Tracking Methods: Temporal and Spatial Features. JSDP 2025; 22 (1) :83-112
URL: http://jsdp.rcisp.ac.ir/article-1-1409-fa.html
بیات محمدحسین، تارویردی زاده بهرام، شهبازی محمد. مروری بر روش‌های ردیابی مبتنی بر بینایی؛ ویژگی‌های زمانی و مکانی. پردازش علائم و داده‌ها. 1404; 22 (1) :83-112

URL: http://jsdp.rcisp.ac.ir/article-1-1409-fa.html


استادیار گروه ساخت و تولید، دانشکده مکانیک، دانشگاه علم و صنعت، تهران، ایران
چکیده:   (211 مشاهده)
ردیابی مبتنی بر بینایی که یکی از پرچالش‌ترین زمینه‌های موجود در بینایی ماشین است به معنای دنبال‌کردن یک یا چند هدف در دنباله‌ای از تصاویر است؛ چالش‌هایی نظیر تغییر در ظاهر هدف، پوشانده‌شدن با عوامل محیطی، حرکات سریع و ناگهانی که هر یک زمینه پژوهشی فعالی را به خود اختصاص داده‌اند. دو شاخص مهم در عملکرد یک ردیاب، سرعت اجرا و دقت آن هستند. با ساده‌ترشدن الگوریتم سرعت افزایش‌ می‌یابد؛ اما از دقت کاسته می‌شود و تعامل میان این دو، موضوع مهمی به‌ویژه در پیاده‌سازی‌های عملی است. در این پژوهش به بررسی جامع و پیاده‌سازی الگوریتم‌های ردیابی مختلف پرداخته و روش‌های مناسب با کاربردهای عملی معرفی شده‌است. از سوی دیگر ساختارهای مختلف ردیابی بررسی و بر اساس ویژگی‌های مکانی، زمانی، بصری و حرکتی دسته‌بندی شده‌اند؛ همچنین به‌دلیل توسعه روش‌های یادگیری عمیق و تأثیر آن‌ها در ردیابی، معماری‌های عمیق، پایگاه‌های داده، مجموعه‌های آموزشی، روش‌ها و استانداردهای ارزیابی معرفی شده و افق پیشِ‌روی این حوزه مورد بحث قرار گرفته‌است. بررسی‌ها نشان می‌دهد که ویژگی‌های زمانی و حرکتی علی‌رغم تأثیر مطلوب بر عملکرد ردیابی کمتر مورد توجه قرار گرفته‌اند. با توسعه شبکه‌های عمیق حافظه‌دار استفاده از این ویژگی‌ها روبه‌افزایش بوده و سهم بیشتری را در ردیابی به خود اختصاص داده‌اند؛ ازاین‌رو ویژگی‌های زمانی و حرکتی با تمرکز بیشتری بررسی شده‌اند.
متن کامل [PDF 1605 kb]   (74 دریافت)    
نوع مطالعه: پژوهشي | موضوع مقاله: مقالات پردازش تصویر
دریافت: 1402/9/3 | پذیرش: 1403/9/14 | انتشار: 1404/3/31 | انتشار الکترونیک: 1404/3/31

فهرست منابع
1. M. Biglari, A. Soleimani, and H. Hassanpour, "Using Discriminative Parts for Vehicle Make and Model Recognition," Signal Data Process., vol. 15, no. 1, 2018, doi: 10.29252/jsdp.15.1.41. [DOI:10.29252/jsdp.15.1.41]
2. J. F. Henriques, R. Caseiro, P. Martins, and J. Batista, "Exploiting the circulant structure of tracking-by-detection with kernels," in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, pp. 702-715. doi: 10.1007/978-3-642-33765-9_50. [DOI:10.1007/978-3-642-33765-9_50]
3. Y. Lecun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no. 7553, pp. 436-444, 2015, doi: 10.1038/nature14539. [DOI:10.1038/nature14539] [PMID]
4. P. Li, D. Wang, L. Wang, and H. Lu, "Deep visual tracking: Review and experimental comparison," Pattern Recognit, vol. 76, pp. 323-338, 2018, doi: 10.1016/j.patcog.2017.11.007. [DOI:10.1016/j.patcog.2017.11.007]
5. A. Sadeghian, A. Alahi, and S. Savarese, "Tracking the Untrackable: Learning to Track Multiple Cues with Long-Term Dependencies," Proceedings of the IEEE International Conference on Computer Vision, vol. 2017-Octob, pp. 300-311, 2017, doi: 10.1109/ICCV.2017.41. [DOI:10.1109/ICCV.2017.41]
6. W. Liu et al., "SSD: Single shot multibox detector," in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, pp. 21-37. doi: 10.1007/978-3-319-46448-0_2. [DOI:10.1007/978-3-319-46448-0_2]
7. J. S. He, Kaiming, Xiangyu Zhang, Shaoqing Ren, "Deep residual learning for image recognition," In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778, 2016. [DOI:10.1109/CVPR.2016.90] [PMID]
8. K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, pp. 1-14, 2015.
9. Krizhevsky Alex, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Adv Neural Inf Process Syst, pp. 145-151, 2012, doi: 10.1145/3383972.3383975. [DOI:10.1145/3383972.3383975]
10. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), New York, NY, USA: ACM, Sep. 2016, pp. 779-788. [DOI:10.1109/CVPR.2016.91]
11. J. Redmon and A. Farhadi, "Yolov3: An incremental improvement," arXiv preprint arXiv:1804.02767, 2018.
12. S. Ren, K. He, R. Girshick, and J. Sun, "Faster r-cnn: Towards real-time object detection with region proposal networks," Adv Neural Inf Process Syst, vol. 28, pp. 91-99, 2015.
13. R. He, K., Gkioxari, G., Dollár, P., & Girshick, "Mask r-cnn," In Proceedings of the IEEE international conference on computer vision, pp. 2961-2969, 2017. [DOI:10.1109/ICCV.2017.322]
14. D. Zhang, H. Maei, X. Wang, and Y.-F. Wang, "Deep reinforcement learning for visual object tracking in videos," arXiv preprint arXiv:1701.08936, 2017.
15. R. Spilger et al., "A Recurrent Neural Network for Particle Tracking in Microscopy Images Using Future Information, Track Hypotheses, and Multiple Detections," IEEE Transactions on Image Processing, vol. 29, pp. 3681-3694, 2020, doi: 10.1109/TIP.2020.2964515. [DOI:10.1109/TIP.2020.2964515] [PMID]
16. T. Yang and A. B. Chan, "Learning dynamic memory networks for object tracking," Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11213 LNCS, pp. 153-169, 2018, doi: 10.1007/978-3-030-01240-3_10. [DOI:10.1007/978-3-030-01240-3_10]
17. L. Bertinetto, J. Valmadre, J. F. Henriques, A. Vedaldi, and P. H. S. Torr, "Fully-convolutional siamese networks for object tracking," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9914 LNCS, pp. 850-865, 2016, doi: 10.1007/978-3-319-48881-3_56. [DOI:10.1007/978-3-319-48881-3_56]
18. G. Plastiras, C. Kyrkou, and T. Theocharides, "You Only Look Once: Unified, Real-Time Object Detection," ArXiv, 2019.
19. K. Remya and C. V Vipin Krishnan, "Survey of Generative and Discriminative Appearance Models in Visual Object Tracking," International Journal of Advance Research, Ideas and Innovations in Technology, vol. 4, no. 1, pp. 343-346, 2018.
20. Y. Wu, J. Lim, and M. H. Yang, "Online object tracking: A benchmark," Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2411-2418, 2013, doi: 10.1109/CVPR.2013.312. [DOI:10.1109/CVPR.2013.312] [PMID] []
21. Y. Wu, J. Lim, and M. H. Yang, "Object tracking benchmark," IEEE Trans Pattern Anal Mach Intell, vol. 37, no. 9, pp. 1834-1848, 2015, doi: 10.1109/TPAMI.2014.2388226. [DOI:10.1109/TPAMI.2014.2388226] [PMID]
22. H. J. C. Kristan, Matej, Aleš Leonardis, Jiří Matas, Michael Felsberg, Roman Pflugfelder, Joni-Kristian Kämäräinen, "The Tenth Visual Object Tracking VOT2022 Challenge Results," In Computer Vision-ECCV 2022 Workshops, pp. 431-460, 2023.
23. K. H. Huang, Lianghua, Xin Zhao, "Got-10k: A large high diversity benchmark for generic object tracking in the wild," IEEE Trans Pattern Anal Mach Intell, pp. 1562-1577, 2019. [DOI:10.1109/TPAMI.2019.2957464] [PMID]
24. H. L. Fan, Heng, Liting Lin, Fan Yang, Peng Chu, Ge Deng, Sijia Yu, Hexin Bai, Yong Xu, Chunyuan Liao, "Lasot: A high-quality benchmark for large-scale single object tracking," In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 5374-5383, 2019. [DOI:10.1109/CVPR.2019.00552]
25. B. G. Muller, Matthias, Adel Bibi, Silvio Giancola, Salman Alsubaihi, "Trackingnet: A large-scale dataset and benchmark for object tracking in the wild," In Proceedings of the European conference on computer vision (ECCV), pp. 300-317, 2018. [DOI:10.1007/978-3-030-01246-5_19]
26. S. L. Kiani Galoogahi, Hamed, Ashton Fagg, Chen Huang, Deva Ramanan, "Need for speed: A benchmark for higher frame rate object tracking," In Proceedings of the IEEE International Conference on Computer Vision, pp. 1125-1134, 2017. [DOI:10.1109/ICCV.2017.128]
27. B. G. Mueller, Matthias, Neil Smith, "A benchmark and simulator for uav tracking," In Computer Vision-ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part I 14, Springer International Publishing, pp. 445-461, 2016. [DOI:10.1007/978-3-319-46448-0_27]
28. M. S. et al Fan, Heng, Longyin Wen, Dawei Du, Pengfei Zhu, Qinghua Hu, Haibin Ling, "Visdrone-sot2020: The vision meets drone single object tracking challenge results," In Computer Vision-ECCV 2020 Workshops: Glasgow, UK, August 23-28, 2020, Proceedings, Part IV 16, pp. 728-749. Springer International Publishing, 2020. [DOI:10.1007/978-3-030-66823-5_44]
29. J. Z. et al Chen, Guanlin, Wenguan Wang, Zhijian He, Lujia Wang, Yixuan Yuan, Dingwen Zhang, "VisDrone-MOT2021: The vision meets drone multiple object tracking challenge results," In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2839-2846, 2021. [DOI:10.1109/ICCVW54120.2021.00318]
30. M. S. et al Du, Dawei, Longyin Wen, Pengfei Zhu, Heng Fan, Qinghua Hu, Haibin Ling, "Visdrone-cc2020: The vision meets drone crowd counting challenge results," In Computer Vision-ECCV 2020 Workshops: Glasgow, UK, August 23-28, 2020, Proceedings, Part IV 16, pp. 675-691. Springer International Publishing, 2020. [DOI:10.1007/978-3-030-66823-5_41]
31. Q. T. Du, Dawei, Yuankai Qi, Hongyang Yu, Yifan Yang, Kaiwen Duan, Guorong Li, Weigang Zhang, Qingming Huang, "The unmanned aerial vehicle benchmark: Object detection and tracking," In Proceedings of the European conference on computer vision (ECCV), pp. 370-386, 2018.
32. L. L.-T. Patrick Dendorfer, Hamid Rezatofighi, Anton Milan, Javen Shi, Daniel Cremers, Ian Reid, Stefan Roth, Konrad Schindler, "MOT20: A benchmark for multi object tracking in crowded scenes," arXiv:2003.09003, 2020.
33. D. R. Achal Dave, Tarasha Khurana, Pavel Tokmakov, Cordelia Schmid, "Tao: A large-scale benchmark for tracking any object," In European Conference on Computer Vision, 2020.
34. N. S. Lin, Weiyao, Huabin Liu, Shizhan Liu, Yuxi Li, Rui Qian, Tao Wang, Ning Xu, Hongkai Xiong, Guo-Jun Qi, "Human in events: A large-scale benchmark for human-centric video analysis in complex events," arXiv preprint arXiv:2005.04490, 2020.
35. M. Kristan et al., "The Eighth Visual Object Tracking VOT2020 Challenge ResultsKristan, M., Leonardis, A., Matas, J., Felsberg, M., Pflugfelder, R., Kämäräinen, J.-K., Danelljan, M., Zajc, L. Č., Lukežič, A., Drbohlav, O., He, L., Zhang, Y., Yan, S., Yang, J., Fernández, G.," pp. 547-601, 2020. [DOI:10.1007/978-3-030-68238-5_39]
36. Y. Wu, J. Lim, and M. H. Yang, "Object tracking benchmark," IEEE Trans Pattern Anal Mach Intell, vol. 37, no. 9, pp. 1834-1848, 2015, doi: 10.1109/TPAMI.2014.2388226. [DOI:10.1109/TPAMI.2014.2388226] [PMID]
37. D. Gordon, A. Farhadi, and D. Fox, "Re3 : Real-Time Recurrent Regression Networks for Object Tracking," IEEE Robot Autom Lett, vol. 3, pp. 788-795, 2018. [DOI:10.1109/LRA.2018.2792152]
38. G. Ciaparrone, F. Luque Sánchez, S. Tabik, L. Troiano, R. Tagliaferri, and F. Herrera, "Deep learning in video multi-object tracking: A survey," Neurocomputing, vol. 381, pp. 61-88, 2020, doi: 10.1016/j.neucom.2019.11.023. [DOI:10.1016/j.neucom.2019.11.023]
39. J. Fan, W. Xu, Y. Wu, and Y. Gong, "Human tracking using convolutional neural networks," IEEE Trans Neural Netw, vol. 21, no. 10, pp. 1610-1623, 2010, doi: 10.1109/TNN.2010.2066286. [DOI:10.1109/TNN.2010.2066286] [PMID]
40. T. T. Trinh, R. Yoshihashi, R. Kawakami, M. Iida, and T. Naemura, "Bird detection near wind turbines from high-resolution video using lstm networks," World Wind Energy Conference, 2016.
41. H. Fan and H. Ling, "SANet: Structure-Aware Network for Visual Tracking," IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 2017-July, pp. 2217-2224, 2017, doi: 10.1109/CVPRW.2017.275. [DOI:10.1109/CVPRW.2017.275] [PMID]
42. F. Bi et al., "Review on Video Object Tracking Based on Deep Learning," Journal of New Media, vol. 1, no. 2, pp. 63-74, 2019, doi: 10.32604/jnm.2019.06253. [DOI:10.32604/jnm.2019.06253]
43. X. Yang, C. Ma, J.-B. Huang, and M.-H. Yang, "Hierarchical Convolutional Features for Visual Tracking," Proceedings of the IEEE international conference on computer vision, pp. 3074-3082, 2015, doi: 10.1109/ICCV.2015.352. [DOI:10.1109/ICCV.2015.352]
44. D. Held, S. Thrun, and S. Savarese, "Learning to track at 100 FPS with deep regression networks," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9905 LNCS, pp. 749-765, 2016, doi: 10.1007/978-3-319-46448-0_45. [DOI:10.1007/978-3-319-46448-0_45]
45. G. E. H. Krizhevsky, Alex, Ilya Sutskever, "Imagenet classification with deep convolutional neural networks," Adv Neural Inf Process Syst, pp. 1-1432, 2012, doi: 10.1201/9781420010749. [DOI:10.1201/9781420010749]
46. N. Mahmoudi, "Multi-target tracking using CNN-based features : CNNMTT," Multimedia Tools and Applications 78.6 (2019): 7077-7096., 2019. [DOI:10.1007/s11042-018-6467-6]
47. N. Wang, S. Li, A. Gupta, and D.-Y. Yeung, "Transferring Rich Feature Hierarchies for Robust Visual Tracking," arXiv preprint arXiv:1501.04587, 2015.
48. L. Wang, W. Ouyang, X. Wang, and H. Lu, "STCT: Sequentially training convolutional networks for visual tracking," Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 1373-1381, 2016, doi: 10.1109/CVPR.2016.153. [DOI:10.1109/CVPR.2016.153]
49. Q. Chu, W. Ouyang, H. Li, X. Wang, B. Liu, and N. Yu, "Online Multi-object Tracking Using CNN-Based Single Object Tracker with Spatial-Temporal Attention Mechanism," Proceedings of the IEEE International Conference on Computer Vision, vol. 2017-Octob, pp. 4846-4855, 2017, doi: 10.1109/ICCV.2017.518. [DOI:10.1109/ICCV.2017.518]
50. A. W. M. Smeulders, D. M. Chu, R. Cucchiara, S. Calderara, A. Dehghan, and M. Shah, "Visual tracking: An experimental survey," IEEE Trans Pattern Anal Mach Intell, vol. 36, no. 7, pp. 1442-1468, 2014, doi: 10.1109/TPAMI.2013.230. [DOI:10.1109/TPAMI.2013.230] [PMID]
51. O. Russakovsky et al., "ImageNet Large Scale Visual Recognition Challenge," Int J Comput Vis, vol. 115, no. 3, pp. 211-252, 2015, doi: 10.1007/s11263-015-0816-y. [DOI:10.1007/s11263-015-0816-y]
52. M. Kristan, "The sixth Visual Object Tracking VOT2018 challenge results," Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 2018.
53. I. Goodfellow, Y. Bengio, and A. Courville, "Deep Learning," p. 800, 2017.
54. C. Szegedy et al., "Going deeper with convolutions," Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 07-12-June, pp. 1-9, 2015, doi: 10.1109/CVPR.2015.7298594. [DOI:10.1109/CVPR.2015.7298594]
55. A. Milan, S. H. Rezatofighi, A. Dick, I. Reid, and K. Schindler, "Online multi-target tracking using recurrent neural networks," in 31st AAAI Conference on Artificial Intelligence, AAAI 2017, 2017, pp. 4225-4232. [DOI:10.1609/aaai.v31i1.11194]
56. S. Hochreiter and J. Urgen Schmidhuber, "Long Short term Memory," Neural Comput, vol. 9, no. 8, p. 17351780, 1997. [DOI:10.1162/neco.1997.9.8.1735] [PMID]
57. G. Ning et al., "Spatially supervised recurrent convolutional neural networks for visual object tracking (ROLO)," Proceedings - IEEE International Symposium on Circuits and Systems, no. 1, pp. 1-4, 2017, doi: 10.1109/ISCAS.2017.8050867. [DOI:10.1109/ISCAS.2017.8050867]
58. K. Fang, "Track-RNN: Joint Detection and Tracking Using Recurrent Neural Networks," 29th Conference on Neural Information Processing Systems (NIPS 2016), no. Nips, 2016.
59. S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," IEEE Trans Pattern Anal Mach Intell, vol. 39, no. 6, pp. 1137-1149, 2017, doi: 10.1109/TPAMI.2016.2577031. [DOI:10.1109/TPAMI.2016.2577031] [PMID]
60. T. Yang and A. B. Chan, "Visual Tracking via Dynamic Memory Networks," IEEE Trans Pattern Anal Mach Intell, vol. 14, no. 8, pp. 1-1, 2019, doi: 10.1109/tpami.2019.2929034. [DOI:10.1109/TPAMI.2019.2929034] [PMID]
61. T. Yang and A. B. Chan, "Recurrent Filter Learning for Visual Tracking," Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017, vol. 2018-Janua, pp. 2010-2019, 2017, doi: 10.1109/ICCVW.2017.235. [DOI:10.1109/ICCVW.2017.235] [PMID]
62. P. Ondrúška and I. Posner, "Deep tracking: Seeing beyond seeing using recurrent neural networks," 30th AAAI Conference on Artificial Intelligence, AAAI 2016, pp. 3361-3367, 2016. [DOI:10.1609/aaai.v30i1.10413]
63. Q. Gan, Q. Guo, Z. Zhang, and K. Cho, "First Step toward Model-Free, Anonymous Object Tracking with Recurrent Neural Networks," arXiv preprint arXiv:1511.06425, pp. 1-13, 2015.
64. S. E. Kahou, V. Michalski, R. Memisevic, C. Pal, and P. Vincent, "RATM: Recurrent Attentive Tracking Model," in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2017, pp. 1613-1622. doi: 10.1109/CVPRW.2017.206. [DOI:10.1109/CVPRW.2017.206]
65. Q. Wang, C. Yuan, J. Wang, and W. Zeng, "Learning attentional recurrent neural network for visual tracking," IEEE Trans Multimedia, vol. 21, no. 4, pp. 930-942, 2019, doi: 10.1109/TMM.2018.2869277. [DOI:10.1109/TMM.2018.2869277]
66. J. Jin, J. Bates, C. Farabet, and E. Culurciello, "Tracking with Deep Neural Networks," 2013 47th Annual Conference on Information Sciences and Systems (CISS). IEEE, no. 1, 2013. [DOI:10.1109/CISS.2013.6552287]
67. S. Hong, T. You, S. Kwak, and B. Han, "Online tracking by learning discriminative saliency map with convolutional neural network," 32nd International Conference on Machine Learning, ICML 2015, vol. 1, pp. 597-606, 2015.
68. G. Koch, "Siamese Neural Networks for One-shot Image Recognition," 2011.
69. Y. Wu, Y. Sui, and G. Wang, "Vision-Based Real-Time Aerial Object Localization and Tracking for UAV Sensing System," IEEE Access, vol. 5, pp. 23969-23978, 2017, doi: 10.1109/ACCESS.2017.2764419. [DOI:10.1109/ACCESS.2017.2764419]
70. G. Zhu, F. Porikli, and H. Li, "Robust Visual Tracking with Deep Convolutional Neural Network based Object Proposals on PETS," Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2016. [DOI:10.1109/CVPRW.2016.160]
71. S. P. Bharati, Y. Wu, Y. Sui, C. Padgett, and G. Wang, "Real-Time Obstacle Detection and Tracking for Sense-and-Avoid Mechanism in UAVs," IEEE Transactions on Intelligent Vehicles, vol. 3, no. 2, pp. 185-197, 2018, doi: 10.1109/tiv.2018.2804166. [DOI:10.1109/TIV.2018.2804166]
72. K. Zhu et al., "Single object tracking in satellite videos: Deep siamese network incorporating an interframe difference centroid inertia motion model," Remote Sens (Basel), vol. 13, no. 7, 2021, doi: 10.3390/rs13071298. [DOI:10.3390/rs13071298]
73. X. Y. Yan, Bin, Xinyu Zhang, Dong Wang, Huchuan Lu, "Alpha-refine: Boosting tracking performance by precise bounding box estimation," In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5289-5298, 2021. [DOI:10.1109/CVPR46437.2021.00525]
74. B. L. Voigtlaender, Paul, Jonathon Luiten, Philip HS Torr, "Siam r-cnn: Visual tracking by re-detection," In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 6578-6588, 2020. [DOI:10.1109/CVPR42600.2020.00661]
75. M. Paul, M. Danelljan, C. Mayer, and L. Van Gool, "Robust Visual Tracking by Segmentation," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13682 LNCS, pp. 571-588, 2022, doi: 10.1007/978-3-031-20047-2_33. [DOI:10.1007/978-3-031-20047-2_33]
76. C. Dicle, O. I. Camps, and M. Sznaier, "The way they move: Tracking multiple targets with similar appearance," Proceedings of the IEEE International Conference on Computer Vision, pp. 2304-2311, 2013, doi: 10.1109/ICCV.2013.286. [DOI:10.1109/ICCV.2013.286]
77. M. Danelljan, G. Häger, F. S. Khan, and M. Felsberg, "Accurate scale estimation for robust visual tracking," BMVC 2014 - Proceedings of the British Machine Vision Conference 2014, doi: 10.5244/c.28.65. [DOI:10.5244/C.28.65]
78. M. Babaee, Z. Li, and G. Rigoll, "Occlusion Handling in Tracking Multiple People Using RNN," Proceedings - International Conference on Image Processing, ICIP, pp. 2715-2719, 2018, doi: 10.1109/ICIP.2018.8451140. [DOI:10.1109/ICIP.2018.8451140]
79. A. Bewley, Z. Ge, L. Ott, F. Ramos, and B. Upcroft, "Simple online and realtime tracking," Proceedings - International Conference on Image Processing, ICIP, vol. 2016-Augus, pp. 3464-3468, 2016, doi: 10.1109/ICIP.2016.7533003. [DOI:10.1109/ICIP.2016.7533003]
80. G. Khan, Z. Tariq, and M. U. G. Khan, "Multi-Person Tracking Based on Faster R-CNN and Deep Appearance Features," Visual Object Tracking in the Deep Neural Networks Era. IntechOpen, vol. i, no. tourism, p. 13, 2019, doi: http://dx.doi.org/10.5772/57353. [DOI:10.5772/57353]
81. N. Wojke, A. Bewley, and D. Paulus, "Simple online and realtime tracking with a deep association metric," Proceedings - International Conference on Image Processing, ICIP, vol. 2017-Septe, pp. 3645-3649, 2018, doi: 10.1109/ICIP.2017.8296962. [DOI:10.1109/ICIP.2017.8296962]
82. H. Nam and B. Han, "Learning Multi-domain Convolutional Neural Networks for Visual Tracking," Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 4293-4302, 2016, doi: 10.1109/CVPR.2016.465. [DOI:10.1109/CVPR.2016.465]
83. I. Jung, J. Son, M. Baek, and B. Han, "Real-Time MDNet," Proceedings of the European Conference on Computer Vision (ECCV), 2018. [DOI:10.1007/978-3-030-01225-0_6]
84. J. T. Shuai, Bing, Andrew Berneshawi, Xinyu Li, Davide Modolo, "Siammot: Siamese multi-object tracking," In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 12372-12382, 2021. [DOI:10.1109/CVPR46437.2021.01219]
85. S. Pellegrini, A. Ess, K. Schindler, and L. Van Gool, "You'll never walk alone: Modeling social behavior for multi-target tracking," Proceedings of the IEEE International Conference on Computer Vision, pp. 261-268, 2009, doi: 10.1109/ICCV.2009.5459260. [DOI:10.1109/ICCV.2009.5459260]
86. J. Qiu, L. Wang, Y. H. Hu, and Y. Wang, "Two motion models for improving video object tracking performance," Computer Vision and Image Understanding, vol. 195, no. March, p. 102951, 2020, doi: 10.1016/j.cviu.2020.102951. [DOI:10.1016/j.cviu.2020.102951]
87. B. Yang and R. Nevatia, "Multi-target tracking by online learning a CRF model of appearance and motion patterns," Int J Comput Vis, vol. 107, no. 2, pp. 203-217, 2014, doi: 10.1007/s11263-013-0666-4. [DOI:10.1007/s11263-013-0666-4]
88. M. Shahbazi, M. H. Bayat, and B. Tarvirdizadeh, "A motion model based on recurrent neural networks for visual object tracking," Image Vis Comput, vol. 126, p. 104533, 2022, doi: 10.1016/j.imavis.2022.104533. [DOI:10.1016/j.imavis.2022.104533]
89. Z. Kang, T. Xu, X. F. Zhu, and X. J. Wu, "Learning Motion-Perceive Siamese network for robust visual object tracking," Pattern Recognit Lett, vol. 173, pp. 23-29, Sep. 2023, doi: 10.1016/j.patrec.2023.07.011. [DOI:10.1016/j.patrec.2023.07.011]
90. H. Zhang, J. Zhang, G. Nie, J. Hu, and W. J. (Chris) Zhang, "Residual memory inference network for regression tracking with weighted gradient harmonized loss," Inf Sci (N Y), vol. 597, pp. 105-124, Jun. 2022, doi: 10.1016/j.ins.2022.03.047. [DOI:10.1016/j.ins.2022.03.047]
91. Z. Zhang, H. Peng, J. Fu, B. Li, and W. Hu, "Ocean: Object-aware Anchor-free Tracking," Jun. 2020. [DOI:10.1007/978-3-030-58589-1_46]
92. J. Wang, C. Lai, W. Zhang, Y. Wang, and C. Meng, "Transformer tracking with multi-scale dual-attention," Complex & Intelligent Systems, vol. 9, no. 5, pp. 5793-5806, Oct. 2023, doi: 10.1007/s40747-023-01043-1. [DOI:10.1007/s40747-023-01043-1]
93. B. Li, W. Wu, Q. Wang, F. Zhang, J. Xing, and J. Yan, "SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks," Dec. 2018. [DOI:10.1109/CVPR.2019.00441]
94. Z. Chen, B. Zhong, G. Li, S. Zhang, and R. Ji, "Siamese Box Adaptive Network for Visual Tracking," Mar. 2020. [DOI:10.1109/CVPR42600.2020.00670] [PMID]
95. M. Danelljan, L. Van Gool, and R. Timofte, "Probabilistic Regression for Visual Tracking," Mar. 2020. [DOI:10.1109/CVPR42600.2020.00721]
96. D. Guo, J. Wang, Y. Cui, Z. Wang, and S. Chen, "SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking," in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Jun. 2020, pp. 6268-6276. doi: 10.1109/CVPR42600.2020. 00630. [DOI:10.1109/CVPR42600.2020]
97. F. Xie, C. Wang, G. Wang, Y. Cao, W. Yang, and W. Zeng, "Correlation-Aware Deep Tracking," Mar. 2022. [DOI:10.1109/CVPR52688.2022.00855]
98. R. U. Geiger, Andreas, Martin Lauer, Christian Wojek, Christoph Stiller, "3d traffic scene understanding from movable platforms," IEEE transactions on pattern analysis and machine intelligence 36, no. 5, pp. 1012-1025, 2013. [DOI:10.1109/TPAMI.2013.185] [PMID]
99. J. M. Rehg. Kim, Chanho, Fuxin Li, Arridhana Ciptadi, "Multiple hypothesis tracking revisited," In Proceedings of the IEEE international conference on computer vision, pp. 4696-4704, 2015. [DOI:10.1109/ICCV.2015.533]
100. A. C. Sanchez-Matilla, Ricardo, Fabio Poiesi, "Online multi-target tracking with strong and weak detections," In Computer Vision-ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part II 14, pp. 84-99. Springer International Publishing, 2016. [DOI:10.1007/978-3-319-48881-3_7]
101. K. Schindler. Milan, Anton, Laura Leal-Taixé, Ian Reid, Stefan Roth, "MOT16: A benchmark for multi-object tracking," arXiv preprint arXiv:1603.00831, 2016.
102. G. Bhat, M. Danelljan, L. Van Gool, and R. Timofte, "Learning discriminative model prediction for tracking," Proceedings of the IEEE International Conference on Computer Vision, vol. 2019-Octob, pp. 6181-6190, 2019, doi: 10.1109/ICCV.2019.00628. [DOI:10.1109/ICCV.2019.00628]

ارسال نظر درباره این مقاله : نام کاربری یا پست الکترونیک شما:
CAPTCHA

ارسال پیام به نویسنده مسئول


بازنشر اطلاعات
Creative Commons License این مقاله تحت شرایط Creative Commons Attribution-NonCommercial 4.0 International License قابل بازنشر است.

کلیه حقوق این تارنما متعلق به فصل‌نامة علمی - پژوهشی پردازش علائم و داده‌ها است.