Area Efficient Moving Object Detection using Spatial and Temporal Method in FPGA

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2022 by IJETT Journal
Volume-70 Issue-9
Year of Publication : 2022
Authors : Sudhir Dagar, Geeta Nijhawan
DOI : 10.14445/22315381/IJETT-V70I9P214

How to Cite?

Sudhir Dagar, Geeta Nijhawan, "Area Efficient Moving Object Detection using Spatial and Temporal Method in FPGA," International Journal of Engineering Trends and Technology, vol. 70, no. 9, pp. 138-147, 2022. Crossref,

Background subtraction has become a challenging task for moving objects. Mainly its complexity of implementation at moving background. Area Efficient hardware implementation of a moving object detection algorithm using FPGAs is described in this paper. The algorithm aims to remove moving items' backgrounds from the CDNet dataset. Even though occlusions aren't detected, simple shadow and highlight reduction are performed. The technique is best implemented on pipelined technology and is optimized for high frame rates. The authors proposed unique improvements, such as background binary mask combinations or non-linear functions in highlight detection, which improved the resiliency and efficiency of hardware implementations. After being developed in FPGA, the method was tested on the CDNet dataset.

Binary Mask, BS, CDNet, FPGA, GMM, etc.

[1] Maddalena, L. and Petrosino, A., “ Background subtraction for moving object detection in RGBD data: A survey,” Journal of Imaging, vol.4, no.5, pp.71.
[2] Garcia-Garcia, B., Bouwmans, T. and Silva, A.J.R, “ Background subtraction in real applications: Challenges, current models and future directions,” Computer Science Review, vol.35, pp.100204.
[3] Xu, Y., Ji, H. and Zhang, W, “Coarse-to-fine sample-based background subtraction for moving object detection,” Optik, vol.207, pp.164195.
[4] Li, W., Zhang, J. and Wang, Y, “ Wepbas: A weighted pixel-based adaptive segmenter for change detection. ,” Sensors, vol.19, no.12, pp.2672.
[5] Iszaidy, I., Ngadiran, R., Ahmad, R.B., Ramli, N., Jais, M.I. and Vijayasarveswari, V, “ An analysis of background subtraction on embedded platform based on synthetic dataset,” In Journal of Physics: Conference Series , Vol. 1755, No. 1, pp. 012042, 2021. IOP Publishing.
[6] Kapoor, S., Pillai, M.S. and Nagpal, A, “Effective Background and Foreground Segmentation Using Unsupervised Frequency Domain Clustering, “ In Proceedings of 3rd International Conference on Computing Informatics and Networks, Springer, Singapore, pp.77-87, 2021.
[7] Akilan, T., Wu, Q.J. and Yang, Y, “ Fusion-based foreground enhancement for background subtraction using multivariate multi-model Gaussian distribution, “ Information Sciences, vol.430, pp.414-431.
[8] Chen, Z., Wang, R., Zhang, Z., Wang, H. and Xu, L, “ Background–foreground interaction for moving object detection in dynamic scenes,” Information Sciences, vol.483, pp.65-81.
[9] Fu, Z., Chen, Y., Yong, H., Jiang, R., Zhang, L. and Hua, X.S, “Foreground gating and background refining network for surveillance object detection,” IEEE Transactions on Image Processing, vol.28, no.12, pp.6077-6090, 2019
[10] Sakthi Ponmanisha R, Mukesh M, Aadhithya N, Prakash Raj G, "Automatic Vehicle Detection and Vehicle Counting," SSRG International Journal of Computer Science and Engineering, vol. 5, no. 1, pp. 15-17, 2018. Crossref,
[11] García‐Lesta, D., López, P., Brea, V.M. and Cabello, D, “ In‐pixel analog memories for a pixel‐based background subtraction algorithm on CMOS vision sensors,” International Journal of Circuit Theory and Applications, vol.46, no.9, pp.1631-1647, 2018.
[12] Zhao, C., Cham, T.L., Ren, X., Cai, J. and Zhu, H, “Background subtraction based on deep pixel distribution learning,” In 2018 IEEE International Conference on Multimedia and Expo (ICME) , IEEE, pp.1-6, 2018.
[13] Wu, Y., Wang, Y., Liu, P., Luo, H., Cheng, B. and Sun, H, “Infrared LSS-target detection via adaptive TCAIE-LGM smoothing and pixel-based background subtraction,” Photonic Sensors, vol.9, no.2, pp.179-188, 2019
[14] Dou, J., Qin, Q. and Tu, Z, “Background subtraction based on deep convolutional neural networks features,” Multimedia Tools and Applications, vol.78, no.11, pp.14549-14571, 2019.
[15] Goyal, K. and Singhai, J, “Review of background subtraction methods using Gaussian mixture model for video surveillance systems,” Artificial Intelligence Review, vol.50, no.2, pp.241-259, 2018.
[16] Mathias, A., Dhanalakshmi, S., Kumar, R. and Narayanamoorthi, R, “Underwater object detection based on bi-dimensional empirical mode decomposition and Gaussian mixture model approach,” Ecological Informatics, vol.66, pp.101469, 2021.
[17] Song, Z., Ali, S. and Bouguila, N, “Bayesian learning of infinite asymmetric gaussian mixture models for background subtraction,” In International Conference on Image Analysis and Recognition , Springer, Cham, pp.264-274, 2019.
[18] Bourouis, S., Pawar, Y. and Bouguila, N, “Entropy-based variational scheme with component splitting for the efficient learning of gamma mixtures,” Sensors, vol.22, no.1, pp.186, 2021.
[19] Zhao, C., Sain, A., Qu, Y., Ge, Y. and Hu, H, “ Background subtraction based on integration of alternative cues in freely moving camera,” IEEE Transactions on Circuits and Systems for Video Technology, vol.29, no.7,pp.1933-1945, 2018.
[20] Singh, R.P., Sharma, P. and Madarkar, J, “ Motion Detection Using a Hybrid Texture-Based Approach,” In Soft Computing for Problem Solving , Springer, Singapore, pp. 609-620, 2020.
[21] Gemignani, G. and Rozza, A, “A robust approach for the background subtraction based on multi-layered self-organizing maps,” IEEE transactions on image processing, vol.25, no.11, pp.5239-5251, 2016.
[22] Zhou, Y. and Ling, B.W.K, “Detecting moving objects via the low-rank representation,” Signal, Image and Video Processing, vol.13, no.8, pp.1593-1601, 2019.
[23] Chen, R., Tong, Y., Yang, J. and Wu, M, “ Video foreground detection algorithm based on fast principal component pursuit and motion saliency,” Computational intelligence and neuroscience, 2019.
[24] Ma, M., Hu, R., Chen, S., Xiao, J. and Wang, Z, “ Robust background subtraction method via low-rank and structured sparse decomposition,” China Communications, vol.15, no.7, pp.156-167, 2018.
[25] Xiu, X., Yang, Y., Liu, W., Kong, L. and Shang, M, “ An improved total variation regularized RPCA for moving object detection with dynamic background,” Journal of Industrial & Management Optimization, vol.16, no.4,pp.1685, 2020.
[26] Pan, P., Wang, Y., Zhou, M., Sun, Z. and He, G, “Background recovery via motion-based robust principal component analysis with matrix factorization,” Journal of Electronic Imaging, vol.27, no.2, pp.023034.
[27] Liu, X. and Zhao, G, “ Background subtraction using multi-channel fused lass,” Electronic Imaging, vol.2019, no.11n pp.269-1, 2019.
[28] Peng, C., Chen, Y., Kang, Z., Chen, C. and Cheng, Q, “Robust principal component analysis: A factorization-based approach with linear complexity,” Information Sciences, vol.513, pp.581-599, 2020.
[29] Fattahi, S. and Sojoudi, S, “Exact guarantees on the absence of spurious local minima for non-negative rank-1 robust principal component analysis,” Journal of machine learning research, 2020.
[30] Chandrakar, R., Raja, R., Miri, R., Sinha, U., Kushwaha, A.K.S. and Raja, H, “ Enhanced the moving object detection and object tracking for traffic surveillance using RBF-FDLNN and CBF algorithm,” Expert Systems with Applications, vol.191, pp.116306., 2022.
[31] Sakpal, N.S. and Sabnis, M, “Adaptive background subtraction in images,” In 2018 International Conference on Advances in Communication and Computing Technology (ICACCT) , IEEE., pp. 439-444, 2018.
[32] Azeez, B. and Alizadeh, F, “ Review and classification of trending background subtraction-based object detection techniques. In 2020 6th International Engineering Conference ,” Sustainable Technology and Development(IEC),IEEE, pp. 185-190, 2020.
[33] Rout, D.K., Subudhi, B.N., Veerakumar, T. and Chaudhury, S, “ Spatio-contextual Gaussian mixture model for local change detection in underwater video,” Expert Systems With Applications, vol.97, pp.117-136, 2018.
[34] Xu, G., Zhang, Y., Zhang, Q., Lin, G., Wang, Z., Jia, Y. and Wang, J, “ Video smoke detection based on deep saliency network,” Fire Safety Journal, vol.105, pp.277-285, 2019.
[35] Garcia-Garcia, B., Bouwmans, T., Sehairi, K. and Zahzah, E.H, “ Real-Time Implementations of Background Subtraction for IoT Applications. In Computer Vision and Internet of Things ,” Chapman and Hall/CRC, pp. 263-286, 2022.
[36] Alipour, P. and Shahbahrami, A, “ An adaptive background subtraction approach based on frame differences in video surveillance,” In 2022 International Conference on Machine Vision and Image Processing (MVIP) , IEEE, pp. 1-5, 2022.
[37] Tah, A., Roy, S., Das, P. and Mitra, A, “ Moving object detection and segmentation using background subtraction by kalman filter,” Indian Journal of Science and Technology, vol.10, no.19, 2017.
[38] Dhatreyee Eluri, A. Raghu Ram, "Smart Maneuvering of Security Camera," SSRG International Journal of Electronics and Communication Engineering, vol. 9, no. 5, pp. 17-20, 2022. Crossref,
[39] Lahraichi, M., Housni, K. and Mbarki, S, “ Foreground Detection using Mean Shift and LBP. Smart Application and Data Analysis for Smart Cities (SADASC'18), “ 2018.
[40] Sakpal, N.S. and Sabnis, M, “Adaptive background subtraction in images, “In 2018 International Conference on Advances in Communication and Computing Technology (ICACCT) , “ IEEE., pp. 439-444, 2018.
[41] Gruosso, M., Capece, N. and Erra, U, “Human segmentation in surveillance video with deep learning,” Multimedia Tools and Applications, vol.80, no.1, pp.1175-1199, 2021.
[42] Bailas, C., Marsden, M., Zhang, D., O'Connor, N.E. and Little, S, “Performance of video processing at the edge for crowd-monitoring applications,” In 2018 IEEE 4th World Forum on Internet of Things (WF-IoT). IEEE, pp. 482-487, 2018.
[43] Oluyide, O.M., Tapamo, J.R. and Walingo, T.M, “Automatic dynamic range adjustment for pedestrian detection in thermal (infrared) surveillance videos,” Sensors, vol.22, no.5, pp.1728.
[44] Pagire, V.R. and Phadke, A.C, “ Detection of Moving Object based on FPGA,” In 2019 IEEE Pune Section International Conference (PuneCon). IEEE, pp. 1-4, 2019.
[45] Goyette, N., Jodoin, P.M., Porikli, F., Konrad, J. and Ishwar, P, “Change detection. net: A new change detection benchmark dataset,” In 2012 IEEE computer society conference on computer vision and pattern recognition ,IEEE, workshops , pp. 1-8, 2012.
[46] Ramya R, Madhura R, "FPGA Implementation of Optimized BIST Architecture for Testing of Logic Circuits," SSRG International Journal of VLSI & Signal Processing, vol.7, no.2, pp. 36-42, 2020. Crossref,
[47] Appiah, K., Hunter, A., Dickinson, P. and Meng, H, “ Accelerated hardware video object segmentation: From foreground detection to connected components labeling,” Computer vision and image understanding, vol.114, no.11, pp.1282-1291, 2010.
[48] Nguyen, Q.C., Vu, V.H. and Thomas, M, “A Kalman filter-based ARX time series modeling for force identification on flexible manipulators,” Mechanical Systems and Signal Processing, vol.169, pp.108743, 2022.
[49] Zhang, G., Yuan, Z., Tong, Q., Zheng, M. and Zhao, J, “ A novel framework for background subtraction and foreground detection. Pattern Recognition,” vol.84, pp.28-38.