Browse > Article
http://dx.doi.org/10.9717/kmms.2020.23.4.517

Real-time Vehicle Tracking Algorithm According to Eigenvector Centrality of Weighted Graph  

Kim, Seonhyeong (School of Computer Science and Engineering, Graduate School, Kyungpook National University)
Kim, Sangwook (School of Computer Science and Engineering, Graduate School, Kyungpook National University)
Publication Information
Journal of Korea Multimedia Society / v.23, no.4, 2020 , pp. 517-524 More about this Journal
Abstract
Recently, many researches have been conducted to automatically recognize license plates of vehicles and use the analyzed information to manage stolen vehicles and track the vehicle. However, such a system must eventually be investigated by people through direct monitoring. Therefore, in this paper, the system of tracking a vehicle is implemented by sharing the information analyzed by the vehicle image among cameras registered in the IoT environment to minimize the human intervention. The distance between cameras is indicated by the node and the weight value of the weighted-graph, and the eigenvector centrality is used to select the camera to search. It demonstrates efficiency by comparing the time between analyzing data using weighted graph searching algorithm and analyzing all data stored in databse. Finally, the path of the vehicle is indicated on the map using parsed json data.
Keywords
Internet of Things; Weighted Graph; Path Generation;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Pokale, A. Tushar, D. T. Ingole, and M. D. Ingole, “A review on real time integrated CCTV system using face detection for vehicle seat vacancy identification with image processing technique,” International Journal on Recent and Innovation Trends in Computing and Communication, Vol. 6, No. 7, pp. 121-123, 2018.
2 S. Yang, C. Choi, and H. Choi, “Design and Implementation of Vehicle Route Tracking System using Hadoop-Based Bigdata Image Processing,” Journal of Digital Contents Society, Vol. 14, No. 4, pp. 447-454, 2013.   DOI
3 A. Paul, "Graph based M2M optimization in an IoT environment," In Proceedings of the 2013 Research in Adaptive and Convergent Systems, pp. 45-46, 2013.
4 C. Caraffi, T. Vojir, J. Trefny, J. Sochman, and J. Matas, "A system for real-time detection and tracking of vehicles from a single carmounted camera," 15th international IEEE conference on intelligent transportation systems, pp. 975-982, 2012.
5 D. M. Babu, K. Manvitha, M. S. Narendra, A. Swathi, and K. P. Varma, “Vehicle tracking using number plate recognition system,” International Journal of Computer Science and Information Technologies, Vol. 6, No. 2, pp. 1473-1476, 2015.
6 Y. Y. Chen, J. R. Chen, L. H. Chang, and S. C. Hung, U.S. Patent No. 9,761,135. Washington, DC: U.S. Patent and Trademark Office, 2017.
7 S. Misra, R. Barthwal, and M. S. Obaidat, "Community detection in an integrated Internet of Things and social network architecture," In 2012 IEEE Global Communications Conference, pp. 1647-1652), 2012.
8 D. Le-Phuoc, H. N. M. Quoc, H. N. Quoc, T. T. Nhat, and M. Hauswirth, "The Graph of Things: A step towards the Live Knowledge Graph of connected things," Journal of Web Semantics, Vol. 37, pp. 25-35, 2016.
9 H. Lee, D. Kim, D. Kim, and S. Y. Bang, "Real-time automatic vehicle management system using vehicle tracking and car plate number identification," IEEE International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No. 03TH8698), Vol. 2, pp. II-353, 2003.
10 Geographical_distance, https://en.wikipedia.org/wiki/Geographical_distance.
11 S. L. Chang, L. S. Chen, Y. C. Chung, and S. W. Chen, “Automatic license plate recognition,” IEEE transactions on intelligent transportation systems, Vol. 5, No. 1, pp. 42-53, 2004.   DOI
12 M. Betke, E. Haritaoglu, and L. S. Davis, "Real-time multiple vehicle detection and tracking from a moving vehicle," Machine vision and applications, Vol. 12(2), pp. 69-83, 2000.   DOI
13 Z. Chen, T. Ellis, and S. A. Velastin, "Vehicle detection, tracking and classification in urban traffic," 15th International IEEE Conference on Intelligent Transportation Systems, pp. 951-956, 2012.
14 D. K. Yadav, L. Sharma, and S. K. Bharti, "Moving object detection in real-time visual surveillance using background subtraction technique," IEEE 14th International Conference on Hybrid Intelligent Systems, pp. 79-84, 2014.
15 M. K. Hossen, and S. H. Tuli, "A surveillance system based on motion detection and motion estimation using optical flow," IEEE 5th International Conference on Informatics, Electronics and Vision (ICIEV), pp. 646-651, 2016.
16 Y. Seungho, and K. Jaemin, “Realtime Vehicle Tracking and Region Detection in Indoor Parking Lot for Intelligent Parking Control,” Journal of Korea Multimedia Society, Vol. 19, No. 2, pp. 418-427, 2016.   DOI