A System for Real-Time Passenger Monitoring System for Bus Rapid Transit System

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• 作者：Jonathan Samuel Lumentut (7)
  Fergyanto E. Gunawan (8)
  Wiedjaja Atmadja (9)
  Bahtiar S. Abbas (9)
  
  7. School of Computer Science ; Bina Nusantara University ; Jakarta ; 11480 ; Indonesia
  8. Binus Graduate Programs ; Bina Nusantara University ; Jakarta ; 11480 ; Indonesia
  9. Faculty of Engineering ; Bina Nusantara University ; Jakarta ; 11480 ; Indonesia
  
• 关键词：Computer vision ; Passenger counting system ; Background substraction ; Bus rapid system
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9012
• 期：1
• 页码：398-407
• 全文大小：2,044 KB
• 参考文献：1. Hartawan, T.: Macet terus, jakarta rugi rp 65 triliun per tahun (2013). http://www.tempo.co/read/news/2013/03/24/214468984/Macet-Terus-Jakarta-Rugi-Rp-65-Triliun-per-Tahun (retrieved on December 2013)
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  4. Chandra, F.Y., Gunawan, F.E., Glann, G., Gunawan, A.A.: Improving the accuracy of real-time traffic data gathered by the floating car data method. In: The 2nd International Conference on Information and Communication Technology (ICoICT), Bandung, Indonesia (2014)
  5. Gunawan, F.E.: Real-time traffic monitoring system. In: International Conference and Advanced Informatics: Concepts, Theory and Applications (ICAICTA), Bandung, Indonesia (2014)
  6. Gunawan, FE, Chandra, FY (2014) Optimal averaging time for predicting traffic velocity using floating car data technique for advanced traveler information system. Procedia - Social and Behavioral Sciences 138: pp. 566-575 CrossRef
  7. Gunawan, F.E., Chandra, F.Y.: Real-Time Traffic Monitoring System using Floating Car method. Lambert Academic Publishing (2014b)
  8. Kang, H-G, Kim, D (2005) Real-time multiple tracking using competitive condensation. Pattern Recognition 38: pp. 1045-1058 CrossRef
  9. Wang, B., Chen, Z., Wang, J., Zhang, L.: A fast passenger detection algorithm based on head area characteristics. In: International Workshop on Information and Electronic Engineering (IWIEE), Harbin, China, pp. 184鈥?88. Elseiver (2012)
  10. Zhou, D (2013) Bus passenger recognition and track of video sequence. Journal of Multimedia 8: pp. 262-269
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  12. Saipullah, K., Anuar, A., Ismail, N., Soo, Y.: Real-time video processing using native programming on android platform. In: IEEE 8th International Colloquium on Signal Processing and Its Applications (2012)
  13. Hofmann, M., Tiefenbacher, P., Rigoll, G.: Background segmentation with feedback: The pixel-based adaptive segmenter. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 38鈥?3 (2012)
  14. Godbehere, A., Matsukawa, A., Goldberg, K.: Visual tracking of human visitors under variable-lighting conditions for a responsive audio art installation. In: American Control Conference (ACC), 4305鈥?312 (2012)
  15. Parks, D., Fels, S.: Evaluation of background subtraction algorithms with post-processing. In: IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance 2008, AVSS 2008, pp. 192鈥?99 (2008)
  16. Weinstock, A., Hook, W., Replogle, M., Cruz, R.: Recapturing global leadership in bus rapid transit: A survey of select u.s. cities. Technical report, Institute for Transportation and Development Policy (2011)
  17. McFarlane, N, Schofield, C (1995) Segmentation and tracking of piglets in images. Machine Vision and Applications 8: pp. 187-193 CrossRef
  
• 作者单位：Intelligent Information and Database Systems
• 丛书名：978-3-319-15704-7
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

TransJakarta is a BRT-based mass transportation system operating in Jakarta, the capital of Indonesia. Currently, the system delivers rather low level of service; as a result, the number of passengers is rather low in comparison to that of the other BRT-based systems. In this work, we propose a design of the passenger counting system for BRT-based system, which is very important for the BRT fleet management to increase the system level of service. The counting system is established by deploying computer vision techniques. A few algorithms are evaluated and the Adaptive Median Filtering with the sampling rate of 13 produces the highest level of precision and recall.