{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T17:13:39Z","timestamp":1770743619756,"version":"3.49.0"},"reference-count":39,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2019,7,22]],"date-time":"2019-07-22T00:00:00Z","timestamp":1563753600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Most approaches for moving object detection (MOD) based on computer vision are limited to stationary camera environments. In advanced driver assistance systems (ADAS), however, ego-motion is added to image frames owing to the use of a moving camera. This results in mixed motion in the image frames and makes it difficult to classify target objects and background. In this paper, we propose an efficient MOD algorithm that can cope with moving camera environments. In addition, we present a hardware design and implementation results for the real-time processing of the proposed algorithm. The proposed moving object detector was designed using hardware description language (HDL) and its real-time performance was evaluated using an FPGA based test system. Experimental results demonstrate that our design achieves better detection performance than existing MOD systems. The proposed moving object detector was implemented with 13.2K logic slices, 104 DSP48s, and 163 BRAM and can support real-time processing of 30 fps at an operating frequency of 200 MHz.<\/jats:p>","DOI":"10.3390\/s19143217","type":"journal-article","created":{"date-parts":[[2019,7,22]],"date-time":"2019-07-22T11:07:28Z","timestamp":1563793648000},"page":"3217","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Moving Object Detection Based on Optical Flow Estimation and a Gaussian Mixture Model for Advanced Driver Assistance Systems"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8208-0419","authenticated-orcid":false,"given":"Jaechan","family":"Cho","sequence":"first","affiliation":[{"name":"School of Electronics and Information Engineering, Korea Aerospace University, Goyang-si 10540, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5332-8980","authenticated-orcid":false,"given":"Yongchul","family":"Jung","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Korea Aerospace University, Goyang-si 10540, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7169-5028","authenticated-orcid":false,"given":"Dong-Sun","family":"Kim","sequence":"additional","affiliation":[{"name":"Korea Electronics Technology Institute, Seongnam-si 463-816, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9344-7052","authenticated-orcid":false,"given":"Seongjoo","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Sejong University, Seoul 143-747, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2299-9911","authenticated-orcid":false,"given":"Yunho","family":"Jung","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Korea Aerospace University, Goyang-si 10540, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MITS.2014.2336271","article-title":"Three decades of driver assistance systems: Review and future perspectives","volume":"6","author":"Bengler","year":"2014","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Khan, M.Q., and Lee, S. (2019). A comprehensive survey of driving monitoring and assistance systems. Sensors, 19.","DOI":"10.3390\/s19112574"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chen, K.P., and Hsiung, P.A. (2018). Vehicle collision prediction under reduced visibility conditions. Sensors, 18.","DOI":"10.3390\/s18093026"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2318","DOI":"10.1109\/TITS.2015.2409109","article-title":"Vehicle detection techniques for collision avoidance systems: A review","volume":"16","author":"Mukhtar","year":"2015","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Sualeh, M., and Kim, G.W. (2019). Dynamic multi-lidar based multiple object detection and tracking. Sensors, 19.","DOI":"10.3390\/s19061474"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5948","DOI":"10.1109\/JSEN.2017.2733223","article-title":"Vehicles detection in complex urban scenes using Gaussian mixture model with FMCW radar","volume":"17","author":"Zhao","year":"2017","journal-title":"IEEE Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1049\/iet-its.2013.0167","article-title":"On creating vision-based advanced driver assistance systems","volume":"9","author":"Nieto","year":"2015","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhan, C., Duan, X., Xu, S., Song, Z., and Luo, M. (2007, January 22\u201324). An improved moving object detection algorithm based on frame difference and edge detection. Proceedings of the Fourth International Conference on Image and Graphics, Chengdu, China.","DOI":"10.1109\/ICIG.2007.153"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"12694","DOI":"10.3390\/s120912694","article-title":"Optimal filter estimation for Lucas-Kanade optical flow","volume":"12","author":"Sharmin","year":"2012","journal-title":"Sensors"},{"key":"ref_10","unstructured":"Stauffer, C., and Grimson, W.E.L. (1999, January 23\u201325). Adaptive background mixture models for real-time tracking. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Fort Collins, CO, USA."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1109\/TPAMI.2005.102","article-title":"Effective Gaussian mixture learning for video background subtraction","volume":"27","author":"Lee","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"219","DOI":"10.2174\/2213275910801030219","article-title":"Background modeling using mixture of Gaussians for foreground detection: A survey","volume":"1","author":"Bouwmans","year":"2008","journal-title":"Recent Pat. Comput. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.1007\/s00138-013-0552-7","article-title":"Video background modeling: Recent approaches, issues and our proposed techniques","volume":"25","author":"Shah","year":"2014","journal-title":"Mach. Vis. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, R., Bunyak, F., Seetharaman, G., and Palaniappan, K. (2014, January 23\u201328). Static and moving object detection using flux tensor with split Gaussian models. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshop, Columbus, OH, USA.","DOI":"10.1109\/CVPRW.2014.68"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1223","DOI":"10.1016\/j.cviu.2010.03.023","article-title":"Light-weight salient foreground detection for embedded smart cameras","volume":"114","author":"Casares","year":"2010","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TCSVT.2012.2202191","article-title":"Efficient moving object detection for lightweight applications on smart cameras","volume":"23","author":"Cuevas","year":"2013","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Azmat, S., Wills, L., and Wills, S. (2014, January 6\u20138). Spatio-temporal multimodal mean. Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation, San Diego, CA, USA.","DOI":"10.1109\/SSIAI.2014.6806034"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1809","DOI":"10.1109\/TCSVT.2013.2269011","article-title":"Fast background subtraction based on a multilayer codebook model for moving object detection","volume":"23","author":"Guo","year":"2013","journal-title":"IEEE Trans. Circuts Syst. Video Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1109\/TPAMI.2012.132","article-title":"Moving object detection by detecting contiguous outliers in the low-rank representation","volume":"35","author":"Zhou","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","unstructured":"Sheikh, Y., Javed, O., and Kanade, T. (October, January 29). Background subtraction for freely moving cameras. Proceedings of the IEEE 12th International Conference on Computer Vision, Kyoto, Japan."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1109\/TCYB.2013.2248057","article-title":"Radial basis function based neural network for motion detection in dynamic scenes","volume":"44","author":"Huang","year":"2013","journal-title":"IEEE Trans. Cybern."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.cviu.2014.06.007","article-title":"Background subtraction for the moving camera: A geometric approach","volume":"127","author":"Zamalieva","year":"2014","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1866","DOI":"10.1007\/s12555-018-0234-3","article-title":"Moving object detection for a moving camera based on global motion compensation and adaptive background model","volume":"17","author":"Jo","year":"2019","journal-title":"Int. J. Control Autom. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/j.sigpro.2015.06.003","article-title":"Autonomous detection and tracking under illumination changes, occlusions and moving camera","volume":"117","author":"Bhaskar","year":"2015","journal-title":"Signal Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.cosrev.2018.03.001","article-title":"New trends on moving object detection in video images captured by a moving camera: A survey","volume":"28","author":"Yazdi","year":"2018","journal-title":"Comput. Sci. Rev."},{"key":"ref_26","unstructured":"Heo, B., Yun, K., and Choi, J. (2011, January 17\u201320). Appearance and motion based deep learning architecture for moving object detection in moving camera. Proceedings of the IEEE International Conference on Image Processing, Beijing, Chaina."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Dike, H.U., Wu, Q., Zhou, Y., and Liang, G. (2018, January 12\u201315). Unmanned aerial vehicle (UAV) based running person detection from a real-time moving camera. Proceedings of the IEEE International Conference on Robotics and Biomimetics, Kuala Lumpur, Malaysia.","DOI":"10.1109\/ROBIO.2018.8665167"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.patcog.2017.09.040","article-title":"A deep convolutional neural network for video sequence background subtraction","volume":"76","author":"Babaee","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kim, D., and Kwon, J. (2016). Moving object detection on a vehicle mounted back-up camera. Sensors, 16.","DOI":"10.3390\/s16010023"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/0004-3702(81)90024-2","article-title":"Determining optical flow","volume":"17","author":"Horn","year":"1981","journal-title":"Artif. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Brox, T., Bruhn, A., Papenberg, N., and Weickert, J. (2004, January 11\u201314). High accuracy optical flow estimation based on a theory for warping. Proceedings of the European Conference on Computer Vision, Prague, Czech Republic.","DOI":"10.1007\/978-3-540-24673-2_3"},{"key":"ref_32","unstructured":"Zach, C., Pock, T., and Bischof, H. (2007, January 12\u201314). A Duality based approach for realtime TV-L1 optical flow. Proceedings of the Joint Pattern Recognition Symposium, Heidelberg, Germany."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lempitsky, V., Roth, S., and Rother, C. (2008, January 23\u201328). FusionFlow: Discrete-continuous optimization for optical flow estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA.","DOI":"10.1109\/CVPR.2008.4587751"},{"key":"ref_34","unstructured":"OpenCV Library (2019, July 15). Source Forge. Available online: https:\/\/sourceforge.net\/projects\/opencvlibrary\/."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2013\/129589","article-title":"FPGA implementation of Gaussian mixture model algorithm for 47fps segmentation of 1080p video","volume":"2013","author":"Genovese","year":"2013","journal-title":"J. Electr. Comput. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1109\/TVLSI.2013.2249295","article-title":"ASIC and FPGA implementation of the Gaussian mixture model algorithm for real-time segmentation of high definition video","volume":"22","author":"Genovese","year":"2014","journal-title":"IEEE Trans. VLSI Syst."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Arivazhagan, S., and Kiruthika, K. (2016, January 26\u201328). FPGA implementation of GMM algorithm for background subtractions in video sequences. Proceedings of the International Conference on Computer Vision and Image Processing, Roorkee, India.","DOI":"10.1007\/978-981-10-2107-7_33"},{"key":"ref_38","unstructured":"Krishnamoorthy, A., and Menon, D. (2013, January 26\u201328). Matrix inversion using Cholesky decomposition. Proceedings of the IEEE Conference on Signal Processing: Algorithms, Architectures, Arrangements, and Applications, Poznan, Poland."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1007\/s11263-008-0136-6","article-title":"Particle video: Long-range motion estimation using point trajectories","volume":"80","author":"Sand","year":"2008","journal-title":"Int. J. Comput. Vis."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/14\/3217\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:08:14Z","timestamp":1760188094000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/14\/3217"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,22]]},"references-count":39,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["s19143217"],"URL":"https:\/\/doi.org\/10.3390\/s19143217","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,22]]}}}