Browse > Article
http://dx.doi.org/10.7472/jksii.2020.21.2.9

A study on counting number of passengers by moving object detection  

Yoo, Sang-Hyun (Department of Convergence Software, KyungMin University)
Publication Information
Journal of Internet Computing and Services / v.21, no.2, 2020 , pp. 9-18 More about this Journal
Abstract
In the field of image processing, a method of detecting and counting passengers as moving objects when getting on and off the bus has been studied. Among these technologies, one of the artificial intelligence techniques, the deep learning technique is used. As another method, a method of detecting an object using a stereo vision camera is also used. However, these techniques require expensive hardware equipment because of the computational complexity of used to detect objects. However, most video equipments have a significant decrease in computational processing power, and thus, in order to detect passengers on the bus, there is a need for an image processing technology suitable for various equipment using a relatively low computational technique. Therefore, in this paper, we propose a technique that can efficiently obtain the number of passengers on the bus by detecting the contour of the object through the background subtraction suitable for low-cost equipment. Experiments have shown that passengers were counted with approximately 70% accuracy on lower-end machines than those equipped with stereo vision camera.
Keywords
Moving Object Detection; Object Contours Detection; Bus Passenger Counting;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 KaewTraKulPong, Pakorn, and Richard Bowden. "An improved adaptive background mixture model for real-time tracking with shadow detection." Videobased surveillance systems. Springer, Boston, MA, pp. 135-144, 2002. http://info.ee.surrey.ac.uk/Research/VSSP/Publications/papers/KaewTraKulPong-AVBS01.pdf
2 Zivkovic, Zoran, and Ferdinand Van Der Heijden. "Efficient adaptive density estimation per image pixel for the task of background subtraction." Pattern recognition letters 27.7, pp. 773-780, 2006. https://doi.org/10.1016/j.patrec.2005.11.005   DOI
3 ZIVKOVIC, Zoran. Improved adaptive Gaussian mixture model for background subtraction. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004. IEEE, pp. 28-31, 2004. https://doi.org/10.1109/ICPR.2004.1333992
4 GODBEHERE, Andrew B.; MATSUKAWA, Akihiro; GOLDBERG, Ken. Visual tracking of human visitors under variable-lighting conditions for a responsive audio art installation. In: 2012 American Control Conference (ACC). IEEE, pp. 4305-4312, 2012. https://doi.org/10.1109/ACC.2012.6315174
5 YE, Sang-Myoung; PARK, Rae-Hong. Accurate segmentation of moving objects using object contours. In: ITC-CSCC: International Technical Conference on Circuits Systems, Computers and Communications. pp. 1138-1139, 2007. http://www.dbpia.co.kr/Journal/articleDetail?nodeId=NODE01600095
6 Canny, John. "A computational approach to edge detection." Readings in computer vision. Morgan Kaufmann, pp. 184-203, 1987. https://doi.org/10.1109/TPAMI.1986.4767851
7 Young-Bong Jung, Dae-Seong Kang. (2010). Image Noise Reduction Using Block-based Gaussian Filter. Proceedings of KIIT Conference, pp. 467-469, 2010. http://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE01455088&language=ko_KR
8 Park, Eun-Soo, et al. "Implementation of Fast Sobel Edge Detector Using SSE Instructions." Proceedings of the KIEE Conference. The Korean Institute of Electrical Engineers, pp. 113-114, 2007. http://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE01975452
9 SSUZUKI, Satoshi, et al. Topological structural analysis of digitized binary images by border following. Computer vision, graphics, and image processing, pp. 32-46, 1985, 30.1. https://doi.org/10.1016/0734-189X(85)90016-7   DOI
10 ANDERSSON SANTIAGO, Gabriel; FAVRE, Martin. DesinoBot: The construction of a color tracking turret. 2015. https://kth.diva-portal.org/smash/get/diva2:915902/FULLTEXT01.pdf
11 Kim, Sangduk, et al. "Bounding Box based Shadow Ray Culling Method for Real-Time Ray Tracer." Journal of Korea Game Society 13.3, pp. 85-94, 2013. http://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE07437775   DOI
12 Kim, Dong-Woo, et al. "Object Detection Method for The Wild Pig Surveillance System." The Journal of The Institute of Internet, Broadcasting and Communication 10.5, pp.229-235, 2010. http://ocean.kisti.re.kr/downfile/volume/iwitt/OTNBBE/2010/v10n5/OTNBBE_2010_v10n5_229.pdf
13 Parekh, Himani S., Darshak G. Thakore, and Udesang K. Jaliya. "A survey on object detection and tracking methods." International Journal of Innovative Research in Computer and Communication Engineering 2.2, pp. 2970-2979, 2014. http://www.rroij.com/open-access/a-survey-on-object-detection-and-tracking-methods.pdf
14 Athanesious, J. Joshan, and P. Suresh. "Systematic survey on object tracking methods in video." International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) 1.8, pp. 242-247, 2012. http://ijarcet.org/wp-content/uploads/IJARCET-VOL-1-ISSUE-8-242-247.pdf
15 Kaur, Manpreet, and Abha Choubey. "A Survey of Object Tracking and Detection Techniques." Journal of Artificial Intelligence Research & Advances 1.3, pp. 24-27, 2015. http://computers.stmjournals.com/index.php?journal=JoAIRA&page=article&op=view&path%5B%5D=284
16 Park, Jong-Hyun, et al. "Moving object detection using clausius entropy and adaptive Gaussian mixture model." Journal of the Institute of Electronics Engineers of Korea CI 47.1, pp. 22-29, 2010. http://ocean.kisti.re.kr/downfile/volume/ieek/DHJJMM/2010/v47n1/DHJJMM_2010_v47n1_22.pdf
17 Jang, In-Tae, et al. "Real Time Object Tracking Method using Multiple Cameras." Journal of the Korea Industrial Information Systems Research 17.4, pp. 51-59, 2012. http://ocean.kisti.re.kr/downfile/volume/ksiis/SOJBB3/2012/v17n4/SOJBB3_2012_v17n4_51.pdf   DOI
18 Kim, Jin Su, and Sung Bum Pan. "Real-Time Loitering Detection using Object Feature." Smart Media Journal 5.3, pp. 93-98, 2016. http://scholar.googleusercontent.com/scholar?q=cache:rePOqC08vTUJ:scholar.google.com/&hl=ko&as_sdt=0,5&scioq=A+Survey+of+Object+Tracking+and+Detection+Techniques
19 Min, JiHong, Jung-Chul Kim, and Kicheon Hong. "Implementation of Drowsiness Driving Warning System based on Eyes Detection and Pupi1 Tracking." Proceedings of the Korean Institute of Intelligent Systems Conference. Korean Institute of Intelligent Systems, pp. 249-252, 2005. http://www.koreascience.or.kr/article/CFKO200508824092919.page
20 Jeon, Ji-Hye, et al. "A Study on Object Detection Algorithm for Abandoned and Removed Objects for Real-time Intelligent Surveillance System." The Journal of Korean Institute of Communications and Information Sciences 35.1C, pp. 24-32, 2010. http://ocean.kisti.re.kr/downfile/volume/kics/GCSHCI/ 2010/v35n1C/GCSHCI_2010_v35n1C_24.pdf
21 O. Boreiko and V. Teslyuk, "Structural model of passenger counting and public transport tracking system of smart city" 2016 XII International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH), Lviv, pp. 124-126, 2016. http://dx.doi.org/10.1109/MEMSTECH.2016.7507533
22 Lumentut, Jonathan Samuel, and Fergyanto E. Gunawan. "Evaluation of recursive background subtraction algorithms for real-time passenger counting at bus rapid transit system." Procedia Computer Science 59, pp. 445-453, 2015. https://doi.org/10.1016/j.procs.2015.07.565   DOI
23 A. S. A. Nasir, N. K. A. Gharib and H. Jaafar, "Automatic Passenger Counting System Using Image Processing Based on Skin Colour Detection Approach," 2018 International Conference on Computational Approach in Smart Systems Design and Applications (ICASSDA), Kuching, pp. 1-8, 2018. http://dx.doi.org/10.1109/ICASSDA.2018.8477628
24 T. Chen, C. Chen, D. Wang and Y. Kuo, "A People Counting System Based on Face-Detection," 2010 Fourth International Conference on Genetic and Evolutionary Computing, Shenzhen, pp. 699-702, 2010. http://dx.doi.org/10.1109/ICGEC.2010.178
25 Lengvenis, Paulius, et al. "Application of computer vision systems for passenger counting in public transport." Elektronika ir Elektrotechnika 19.3, pp. 69-72, 2013. https://doi.org/10.5755/j01.eee.19.3.1232
26 ERHAN, Dumitru, et al. Scalable object detection using deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2147-2154, 2014. http://openaccess.thecvf.com/content_cvpr_2014/papers/Erhan_Scalable_Object_Detection_2014_CVPR_paper.pdf
27 Li, Feng, et al. "Automatic passenger counting system for bus based on RGB-D video." 2nd Annual International Conference on Electronics, Electrical Engineering and Information Science (EEEIS 2016). Atlantis Press, 2016. https://doi.org/10.2991/eeeis-16.2017.29
28 PERNG, Jau-Woei, et al. The design and implementation of a vision-based people counting system in buses. In: 2016 International Conference on System Science and Engineering (ICSSE). IEEE, pp. 1-3, 2016. https://doi.org/10.1109/ICSSE.2016.7551620
29 CHEN, Chao-Ho, et al. People counting system for getting in/out of a bus based on video processing. In: 2008 Eighth International Conference on Intelligent Systems Design and Applications. IEEE, pp. 565-569, 2008. https://doi.org/10.1109/ISDA.2008.335
30 Tarek Yahiaoui, Louahdi Khoudour, and Cyril Meurie "Real-time passenger counting in buses using dense stereovision," Journal of Electronic Imaging 19(3), 031202 (1 July 2010). https://doi.org/10.1117/1.3455989   DOI
31 Gogwang-eun, and Shimgui-bo. " Trends in Object Recognition and Detection Technology Using Deep Learning." The Journal of Institute of Control, Robotics and Systems 23.3, pp. 17-24, 2017. http://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE07245728
32 REN, Shaoqing, et al. Faster r-cnn: Towards real-time object detection with region proposal networks. In: Advances in neural information processing systems. pp. 91-99, 2015. http://papers.nips.cc/paper/5638-faster-r-cnn-towards-real-time-object-detection-with-region-proposal-networks.pdf
33 VIOLA, Paul; JONES, Michael. Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001. IEEE, pp. I-I, 2001. https://doi.org/10.1109/CVPR.2001.990517
34 Viola, Paul, and Michael Jones. "Robust real-time object detection." International journal of computer vision 4, pp. 34-47, 2001. https://www.hpl.hp.com/techreports/Compaq-DEC/CRL-2001-1.pdf
35 Rakibe, Rupali S., and Bharati D. Patil. "Background subtraction algorithm based human motion detection." International Journal of scientific and research publications 3.5, pp. 2250-3153, 2013. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.414.5782&rep=rep1&type=pdf