Abstract
Object tracking is a subject of great interest in different fields of research. In the process of estimating the trajectory of a target in a displacement sequence, the target is not always located exactly, the process tracking can be accompanied by variations in the position and size of the image. The differences observed can be considered as colored measurement noise (CMN). We treat the.se variations as Gauss-Markov color measurement noise. The standard Kalman, Optimal FIR, Optimal Unbiased FIR, and Unbiased FIR filters are tested in simulated displacement sequences to demonstrate the best performance. The object trajectory estimation process was carried out in two stages: “predict” and “update”. The results showed good performance of the FIR and Kalman filters in the object tracking process under conditions of the process and data noise. While at higher data and process noise values, the FIR filters showed better performance.
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References
Bar-Shalom, Y., Li, X.R., Kirubarajan, T.: Estimation with Applications to Tracking and Navigation. Wiley, New York (2001)
Bishop, A.N., Savkin, A.V., Pathirana, P.N.: Vision-based target tracking and surveillance with robust set-valued state estimation. IEEE Signal Process. Lett. 17(3), 289–292 (2009)
Brown, R.G., Hwang, P.Y.C.: Introduction to Random Signals and Applied Kalman Filtering: With MATLAB Exercises, 4th edn. Wiley, Hooboken (2012)
Burger, W., Burger, M.: Principles of Digital Image Processing, vol. 111. Springer, London (2009)
Choeychuent, K., Kumhomtand, P., Chamnongthait, K.: An efficient implementation of the nearest neighbor based visual objects tracking. In: International Symposium on Intelligent Signal Processing and Communication Systems, pp. 574–577. IEEE, Japan (2006)
Deepak, P., Suresh, S.: Design and utilization of bounding box in human detection and activity identification. emerging ict for bridging the future. In: Satapathy, S., Govardhan, A., Raju, K., Mandal, J. (eds.) Emerging ICT for Bridging the Future - Proceedings of the 49th Annual Convention of the Computer Society of India CSI Volume 2. Advances in Intelligent Systems and Computing, vol. 338, pp. 59–70. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-13731-5_8
Farhadi, A., Redmon, J.: YOLOv3: an incremental improvement computer vision and pattern recognition. arXiv preprint arXiv:1804.02767 (2018)
Grewal, M.S., Andrews, A.: Kalman Filtering: Theory and Practice with MATLAB. Wiley, Hoboken (2014)
Kang, T.K., Mo, Y.H., Pae, D.S., Ahn, C.K., Lim, M.T.: Robust visual tracking framework in the presence of blurring by arbitrating appearance-and feature-based detection measurement. J. Int. Meas. Confed. 95, 50–69 (2017)
Karasulu, B., Korukoglu, S.: A software for performance evaluation and comparison of people detection and tracking methods in video processing. Multimed. Tools Appl. 55(3), 677–723 (2011)
Liang, P., Blasch, E., Ling, H.: Encoding color information for visual tracking: algorithms and benchmark. IEEE Trans. Image Process. 24(12), 5630–5644 (2015)
Murray, S.: Real-time multiple object tracking-a study on the importance of speed. arXiv preprint arXiv:1709.03572 (2017)
Padilla, R., Passos, W., Dias, T., Netto, S., Da Silva, E.: A comparative analysis of object detection metrics with a companion open-source toolkit. Electronics 10(3), 279 (2021)
Parekh, H.S., Thakore, D.G., Jaliya, U.K.: A survey on object detection and tracking methods. Int. J. Innov. Res. Comput. Commun. Eng. 2(2), 2970–2978 (2014)
Parmar, M.: A survey of video object tracking methods. Int. J. Eng. Dev. Res. 4, 519–524 (2016)
Shmaliy, Y.S.: Linear optimal fir estimation of discrete time-invariant state-space models. IEEE Trans. Signal Process. 58(6), 3086–3096 (2010)
Shmaliy, Y.S.: An iterative kalman-like algorithm ignoring noise and initial conditions. IEEE Trans. Signal Process. 59(6), 2465–2473 (2011)
Shmaliy, Y.S., Andrade-Lucio, J., Pale-Ramon, E.G., Ortega-Contreras, J., Morales-Mendoza, L.J., González-Lee, M.: Visual object tracking with colored measurement noise using Kalman and UFIR filters. In: 2020 17th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), pp. 1–6. EEE, Mexico City (2020)
Shmaliy, Y.S., Zhao, S., Ahn, C.: Unbiased FIR filtering: an iterative alternative to Kalman filtering ignoring noise and initial conditions. IEEE Control Syst. Mag. 37(5), 70–89 (2017)
Shmaliy, Y.S., Zhao, S., Ahn, C.K.: Kalman and UFIR state estimation with colored measurement noise using backward Euler method. IET Signal Process. 14(2), 64–71 (2020)
Simon, D.: Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches. Wiley, Hoboken (2006)
Smeulders, A.W.: Visual tracking: An experimental survey. IEEE Trans. Pattern Anal. Mach. Intell. 36(7), 1442–1468 (2013)
Computer Vision Lab 2013 Visual Tracker Benchmark (2013). http://cvlab.hanyang.ac.kr/tracker_benchmark/datasets.html
Yilmaz, A., Javed, O., Shah, M.: Object tracking: A Survey. ACM Comput. Surv. 38(4), 1–45 (2006)
Yoon, Y., Kosaka, A., Kak, A.: A new Kalman-filter-based framework for fast and accurate visual tracking of rigid objects. IEEE Trans. Robot. 24(5), 1238–1251 (2008)
Zhao, S., Shmaliy, Y.S., Liu, F.: Fast Kalman-like optimal unbiased FIR filtering with applications. IEEE Trans. Signal Process. 64(9), 2284–2297 (2016)
Zhao, S., Shmaliy, Y.S., Khan, S., Ji, G.: Iterative form for optimal FIR filtering of time-variant systems. Recent Adv. Electrosci. Comput. 114 (2015)
Zhao, S., Shmaliy, Y.S., Liu, F.: Fast computation of discrete optimal FIR estimates in white Gaussian noise. IEEE Signal Process. Lett. 22(6), 718–722 (2014)
Zhao, S., Shmaliy, Y.S., Ahn, C.: Bias-constrained optimal fusion filtering for decentralized WSN with correlated noise sources. IEEE Trans. Signal Inf. Process. Netw. 4(4), 727–735 (2018)
Zhou, X., Li, Y., He, B., Bai, T.: GM-PHD-based multi-target visual tracking using entropy distribution and game theory. IEEE Trans. Ind. Inform. 10(2), 1064–1076 (2014)
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Pale-Ramon, E.G., Shmaliy, Y.S., Morales-Mendoza, L.J., Lee, M.G. (2022). Finite Impulse Response (FIR) Filters and Kalman Filter for Object Tracking Process. In: S. Shmaliy, Y., Abdelnaby Zekry, A. (eds) 6th International Technical Conference on Advances in Computing, Control and Industrial Engineering (CCIE 2021). CCIE 2021. Lecture Notes in Electrical Engineering, vol 920. Springer, Singapore. https://doi.org/10.1007/978-981-19-3927-3_66
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