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http://dx.doi.org/10.7471/ikeee.2019.23.1.235

Rear-Approaching Vehicle Detection Research using Region of Interesting based on Faster R-CNN  

Lee, Yeung-Hak (Dept. of Computer Engineering, Andong National University)
Kim, Joong-Soo (Dept. of Computer Engineering, Andong National University)
Shim, Jae-Chnag (Dept. of Computer Engineering, Andong National University)
Publication Information
Journal of IKEEE / v.23, no.1, 2019 , pp. 235-241 More about this Journal
Abstract
In this paper, we propose a new algorithm to detect rear-approaching vehicle using the frame similarity of ROI(Region of Interest) based on deep learning algorithm for use in agricultural machinery systems. Since the vehicle detection system for agricultural machinery needs to detect only a vehicle approaching from the rear. we use Faster R-CNN model that shows excellent accuracy rate in deep learning for vehicle detection. And we proposed an algorithm that uses the frame similarity for ROI using constrained conditions. Experimental results show that the proposed method has a detection rate of 99.9% and reduced the false positive values.
Keywords
Deep lerning; Faster r-cnn; Agricultural machine; Vehicle detection; Structure similarity;
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1 H. W. Kang, J. W. Baek, and Y. S. Jeong, "Real-Time Side-Rear Vehicle Detection Algorithm for Blind Spot Warning Systems," KIISE Transactions on Computing Practice, vol. 23, no. 7, pp. 408-416, 2018. DOI: 10.4271/2012-01-0293
2 J. Dobahue, R. Girshick, T. Darrell, and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation," IEEE Internal Conference on Computer Vision and Pattern Recognition, pp. 580-587, 2014.
3 Ross Girshick, "Faster-RCNN," IEEE International Conference on Computer Vision, pp. 1440-1448, 2015. DOI: 10.1109/ICCV.2015.169
4 S. Ren, K. He, R. Gisshick, and J. Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 38, no. 6, pp. 1137-1149, 2017. DOI: 10.1109/TPAMI.2016.2577031
5 F. Quanfu, B. Lisa, and S. Hohn, "A Closer Look at Faster R-CNN for Vehicle Detection," 2016 Intelligent Vehicle Symposium, pp. 124-129, 2016. DOI: 10.1109/IVS.2016.7535375
6 S. C. Hsu, C. L. Huang, and C. H. Chuang, "Vehicle Detection using simplified Fast R-CNN," International Workshop on Advanced Image Technology, 2018. DOI: 10.1109/IWAIT.2018.8369767
7 H. S. Kim and J. S. Park, "intensity-based efficient Video Quality Assessment for Variable bitrate Streaming," Korean Institute of Next Generation Computing, vol. 11, no. 5, pp. 63-71, 2015.
8 C. Chen, "Rear Approaching Vehicle Detection with Microphone," Bachelor's Thesis, Halmstad University, 2013.
9 V. K. Ananthanarayanan, "Audio Based Detection of Rear Approaching Vehicle on a Bicycle," Graduate School Thesis, Rutgers University, 2012.
10 C. T. Chen and Y. S. Chen, "Real-time approaching vehicle detection in blind-spot area," 12th Internal IEEE Conference on intelligent Transportation Systems, 2009. DOI: 10.1109/ITSC.2009.5309876