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http://dx.doi.org/10.12815/kits.2019.18.6.191

Development of an Integrated Traffic Object Detection Framework for Traffic Data Collection  

Yang, Inchul (Integrated Road Management Center, Dept. of Infrastructure Safety Research, KICT)
Jeon, Woo Hoon (Integrated Road Management Center, Dept. of Infrastructure Safety Research, KICT)
Lee, Joyoung (Dept. of Civil and Environmental Engineering, New Jersey Institute of Technology)
Park, Jihyun (Dept. of Civil and Environmental Engineering, New Jersey Institute of Technology)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.18, no.6, 2019 , pp. 191-201 More about this Journal
Abstract
A fast and accurate integrated traffic object detection framework was proposed and developed, harnessing a computer-vision based deep-learning approach performing automatic object detections, a multi object tracking technology, and video pre-processing tools. The proposed method is capable of detecting traffic object such as autos, buses, trucks and vans from video recordings taken under a various kinds of external conditions such as stability of video, weather conditions, video angles, and counting the objects by tracking them on a real-time basis. By creating plausible experimental scenarios dealing with various conditions that likely affect video quality, it is discovered that the proposed method achieves outstanding performances except for the cases of rain and snow, thereby resulting in 98% ~ 100% of accuracy.
Keywords
Deep Learning; Direct Object Detection; Multi Objects Tracking; Computer Vision;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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1 Allen J., Richard Y. D., Jin S.(2004), "Object Tracking Using Cam Shift Algorithm and Multiple Quantized Feature Spaces," '05 Proceedings of the Pan-Sydney Area Workshop on Visual Information Processing, pp.3-7.
2 Henriques J. F., Caseiro R., Martins P. and Batista J.(2015), "High-Speed Tracking with Kernelized Correlation Filters," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, pp.583-596.   DOI
3 Lee K. and Shin H.(2019), "Effect on self-enhancement of deep-learning inference by repeated training of false detection cases in tunnel accident image detection," Journal of Korean Tunnelling and Underground Space Association, vol. 21, no. 3, pp.419-432.   DOI
4 Lee T., Kim K., Yun K., Kim K. and Choi D.(2018), "A Method of Counting Vehicle and Pedestrian Using Deep Learning Based on CCTV," Journal of Korean Institute of Intelligent Systems, vol. 28, no. 3, pp.219-224.   DOI
5 Lukezic A., Vojir T., Cehovin Zajc L., Matas J. and Kristan M.(2017), "Discriminative Correlation Filter with Channel and Spatial Reliability," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.6309-6318.
6 Malik A. A., Khalil A. and Khan H. U.(2013), "Object detection and tracking using background subtraction and connected component labeling," International Journal of Computer Applications, vol. 75, no. 13.
7 Nam S.(2018), Deep learning-based real-time object tracking on CCTV, M.S Thesis, Kwangwoon University, South Korea.
8 Park M., Kim H., Choi H. and Park S.(2019), "A Study on Vehicle Detection and Distance Classification Using Mono Camera Based on Deep Learning," Journal of Korean Institute of Intelligent Systems, vol. 29, no. 2, pp.90-96.   DOI
9 Redmon J., Divvala S., Girshick R. and Farhadi A.(2016), "You only look once: Unified, real-time object detection," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.779-788.
10 Szegedy C., Liu W., Jia Y., Sermanet P., Reed S. E., Anguelov D., Erhan D., Vanhoucke V. and Rabinovich A.(2014), "Going deeper with convolutions," 2015 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-9.
11 Szegedy C., Vincent V., Sergey I., Jonathon S. and Zbigniew W.(2016), "Rethinking the Inception Architecture for Computer Vision," 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp.2818-2826.
12 Yilmaz A., Javed O. and Shah M.(2006), "Object tracking: A survey," ACM Computing Surveys, vol. 38, no. 4, p.13.   DOI
13 Youtube, https://www.youtube.com/watch?v=UM0hX7nomi8, Last Access: 2019.10.31.