Study of characteristics of the algorithm for aerial target acquisition and tracking using video imaging
Published: 24.03.2023
Authors: Bobkov A.V., Belozerova E.D., Pistsov A.M.
Published in issue: #3(135)/2023
DOI: 10.18698/2308-6033-2023-3-2261
Category: Aviation and Rocket-Space Engineering | Chapter: Aircraft Dynamics, Ballistics, Motion Control
The paper considers an algorithm for aerial targets acquisition and tracking using video imaging. This algorithm should be introduced in the radio acquisition and guidance aerial systems for long-range identification of the enemy objects, targeting weapons and interception thereof. Existing methods of tracking the objects by video imaging are presented. The most efficient tracking algorithm was determined, i. e. the MOSSE tracker, which is easy to implement, accurate and high-speed. Several aerial targets flight video sequences were found being different in various characteristics. The found video images were used to study characteristics of the aerial target tracking algorithm based on the MOSSE tracker. Study results are presented, and it is shown that the number of objects in the frame, poor lighting, object motion and rotation could interfere with tracking.
References
[1] Panteleev A.S., Oleinik V.V. Metod vizualnogo multitrekinga v realnom vremeni na osnove korrelyatsionnykh filtrov [Real-time visual multi tracking method based on correlation filters]. In: Міжвідомчий науково-технічний збірник «Адаптивні системи автоматичного управління» [Interdepartmental scientific and technical bulletin “Adaptive systems of automatic control”]. Kiev, Politekhnika, 2018, vol. 1, no. 32, pp. 97–106.
[2] Yuankun Li, Tingfa Xu, Honggao Deng, Guokai Shi, Jie Guo. Adaptive correlation model for visual tracking using keypoints matching and deep convolutional feature. Sensors, 2018, vol. 18 (2), paper no. 653. https://doi.org/10.3390/s18020653
[3] Ross D., Lim J., Lin R., Yang M. Incremental learning for robust visual tracking. IJCV, 2008, vol. 77 (1), pp. 125–141.
[4] Adam A., Rivlin E., Shimshoni I. Robust fragments based tracking using the integral histogram. In: 2006 IEEE Conference on Computer Vision and Pattern Recognition. In (CVPR 2006). New York, USA, July, 2006. https://doi.org/10.1109/CVPR.2006.256
[5] Zhang X., Hu W., Maybank S., Li X. Graph based discriminative learning for robust and efficient object tracking. In: IEEE 12th International Conference on Computer Vision 2007. Rio de Janeiro, Brazil, 14–21 October. https://doi.org/10.1109/ICCV.2007.4409034
[6] Babenko B., Yang M.-H., Belongie S. Visual Tracking with Online Multiple Instance Learning. In: 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009). Miami, Florida, USA, 20–25 June, 2009. https://doi.org/10.1109/CVPR.2009.5206737
[7] Bolme D.S., Draper B.A., Beveridge J.R. Average of Synthetic Exact Filters. In: 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009). Miami, Florida, USA, 20–25 June, 2009. https://doi.org/10.1109/CVPR.2009.5206701
[8] Savvides M., Kumar B., Khosla P. Face verification using correlation filters. In: The 3rd IEEE Automatic Identification Advanced Technologies, 2002, pp. 56–61.
[9] Bolme D.S., Beveridge J.R., Draper B.A., Lui Y.M. Visual object tracking using adaptive correlation filters. In: The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010. San Francisco, CA, USA, 13–18 June, 2010. https://doi.org/10.1109/CVPR.2010.5539960
[10] Jiangning Chen, Zhibo Dai, Juntao Duan, Heinrich Matzinger, Ionel Popescu. Naive Bayes with Correlation Factor for Text Classification Problem. In: 2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA). Boca Raton, FL, USA, 16–19 December, 2019. https://doi.org/10.1109/ICMLA.2019.00177
[11] Tikhonov A.N., Arsenin V.Ya. Metody resheniya nekorrektnykh zadach [Methods for solving incorrect problems]. Moscow, Nauka Publ., 1979, 288 p.
[12] Xiufen Ye, Wenyang Luo, Hanjie Huang, Huiming Xing, Weizheng Zhang. Improved scale adaptive KCF-based method for forward looking sonar image. OCEANS 2021, San Diego, CA, USA, 20–23 September, 2021. https://doi.org/10.23919/OCEANS44145.2021.9706069
[13] Tikhonov A.N., Goncharsky A.V., Stepanov V.V., Yagola A.G. Regulyariziruyuschie algoritmy i apriornaya informatsiya [Regularizing algorithms and a priori information]. Moscow, Nauka Publ., 1983, 200 p.
[14] Yuxin Qian, Hongli Zheng, Dazhi He, Zhexi Zhang, Zongpu Zhang. R-CNN object detection inference with deep learning accelerator. In: 2018 IEEE/CIC International Conference on Communications in China (ICCC Workshops). Beijing, China, 16–18 August, 2018. https://doi.org/10.1109/ICCChinaW.2018.8674519
[15] Redmon J., Divvala S., Girshick R., Farhadi A. You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA, 27–30 June, 2016. https://doi.org/10.1109/CVPR.2016.91
[16] Albawi S., Abed Mohammed T., Al-Zawi S. Understanding of a convolutional neural network. In: 2017 International Conference on Engineering and Technology (ICET). Antalya, Turkey, 21–23 August, 2017. https://doi.org/10.1109/ICEngTechnol.2017.8308186
[17] Baklitsky V.K. Korrelyatsionno-ekstremalnye metody navigatsii i navedeniya [Correlation-extreme methods of navigation and guidance]. Tver, TO Knizhnyi Klub Publ., 2009, 360 p.
[18] Gonzalez R., Woods R., Eddins S. Digital Image Processing Using MATLAB, Gatesmark Publ., 2020 [In Russ.: Gonzalez R., Woods R., Eddins S. Tsifrovaya obrabotka izobrazheniy v srede MATLAB. Moscow, Tekhnosfera Publ., 2006, 616 p.].
[19] Zhe Chen, Zhibin Hong, Dacheng Tao. An experimental survey on correlation filter-based tracking. IEEE, 2015. Available at: https://arxiv.org/pdf/1509.05520.pdf (accessed March 19, 2022).
[20] Ling Zhang, Wei Zhou, Jingwei Li, Juan Li, Xin Lou. Histogram of oriented gradients feature extraction without normalization. In: 2020 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS). Ha Long, Vietnam, 8–10 December, 2020. https://doi.org/10.1109/APCCAS50809.2020.9301715
[21] Md. Mehedi Hasan, Arifur Rahaman, Munmun Talukder, Mobarakol Islam, Mirza Md. Shahriar Maswood, Md. Mostafizur Rahman. Neural network performance analysis using hanning window function as dynamic learning rate. In: 2013 International Conference on Informatics, Electronics and Vision (ICIEV). Dhaka, Bangladesh, 17–18 May, 2013. https://doi.org/10.1109/ICIEV.2013.6572609
[22] Press W., Flannery B., Teukolsky S., Vetterling W. Numerical Recipes in C. Cambridge Univ. Press, 1988, 735 p.
[23] Nussbaumer H. Fast Fourier Transform and Convolution Algorithms. Springer, 2nd edition, 1982 [In Russ.: Nussbaumer G. Bystroe preobrazovanie Furie i algoritmy vychisleniya svertok. Moscow, Radio i Svyaz Publ., 1985, 248 p.].
[24] Yijin Yang, Yihong Zhang, Demin Liorcid, Zhijie Wang. Parallel correlation filters for real-time visual tracking. Sensors, 2019, vol. 19 (10), paper 2362. https://doi.org/10.3390/s19102362
[25] Javed O., Rasheed Z., Shafique K., Shah M. Tracking across multiple cameras with disjoint views. In: Proceedings Ninth IEEE International Conference on Computer Vision, 2003, vol. 2, pp. 952–957. DOI: 10.1109/ICCV.2003.1238451
[26] Wang Y., Lu K., Zhai R. Challenge of multi-camera tracking. In: 2014 7th International Congress on Image and Signal Processing. IEEE, 2014, October, pp. 32–37.
[27] Bobkov A.V., Tedeev G.V. Sistema multikamernogo otslezhivaniya obyektov [Multi-camera object tracking system]. Inzhenerny zhurnal: nauka i innovatsii — Engineering Journal: Science and Innovation, 2021, iss. 10 (118). https://doi.org/10.18698/2308-6033-2021-10-2123
[28] Mei Han, A. Sethi, Wei Hua, Yihong Gong. A detection-based multiple object tracking method. In: 2004 International Conference on Image Processing. Singapore, 24–27 October, 2004. https://doi.org/10.1109/ICIP.2004.1421760
[29] Hubenko A., Fonoberov V., Mathew G., Mezic I. Multiscale Adaptive Search. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2011, vol. 41, pp. 1076–1087.
[30] Xiaoxin Guo, Zhiwen Xu, Yinan Lu, Yunjie Pang. An Application of Fourier-Mellin Transform in Image Registration. In: The Fifth International Conference on Computer and Information Technology. Shanghai, China, September 21–23, 2005. https://doi.org/10.1109/CIT.2005.62