참고문헌
- Angelova A., Krizhevsky A., Vanhoucke V., Ogale A. S. and Ferguson D. (2015, September), "Real-Time Pedestrian Detection with Deep Network Cascades," In BMVC, vol. 2, p.4.
- Bay H., Tuytelaars T. and Van Gool L. (2006), "Surf: Speeded up robust features," In European conference on computer vision, Springer, Berlin, Heidelberg, pp.404-417.
- Chen L. C., Hsieh J. W., Lai W. R., Wu C. X. and Chen S. Y. (2010, October), "Vision-based vehicle surveillance and parking lot management using multiple cameras," In 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IEEE, pp.631-634.
- Dalal N. and Triggs B. (2005), "Histograms of oriented gradients for human detection," In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference, IEEE, vol. 1, pp.886-893.
- Hadsell R., Sermanet P., Ben J., Erkan A., Scoffier M., Kavukcuoglu K. and LeCun Y. (2009), "Learning long-range vision for autonomous off-road driving," Journal of Field Robotics, vol. 26, no. 2, pp.120-144. https://doi.org/10.1002/rob.20276
- Hasegawa T., Nohsoh K. and Ozawa S. (1994), "Counting cars by tracking moving objects in the outdoor parking lot," In Vehicle Navigation and Information Systems Conference, 1994. Proceedings, IEEE, pp.63-68.
- He K., Zhang X., Ren S. and Sun, J. (2016), "Deep residual learning for image recognition," In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.770-778.
- John V., Yoneda K., Qi B., Liu Z. and Mita S. (2014, October), "Traffic light recognition in varying illumination using deep learning and saliency map," In Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference, IEEE, pp.2286-2291.
- Krizhevsky A., Sutskever I. and Hinton G. E. (2012), "Imagenet classification with deep convolutional neural networks," In Advances in neural information processing systems, pp.1097-1105.
- Lindeberg T. (2012), Scale invariant feature transform.
- Liu W., Anguelov D., Erhan D., Szegedy C., Reed S., Fu C. Y. and Berg A. C. (2016, October), "Ssd: Single shot multibox detector," In European conference on computer vision, Springer, Cham, pp.21-37.
- Onoro-Rubio D. and Lopez-Sastre R. J. (2016), "Towards perspective-free object counting with deep learning," In European Conference on Computer Vision, Springer, Cham, pp.615-629.
- OpenCV: Detection of ArUco Markers. (n.d.), Retrieved September 11, 2018, from https://docs.opencv.org/3.1.0/d5/dae/tutorial_aruco_detection.html.
- Pietikainen M. (2010), "Local binary patterns," Scholarpedia, vol. 5, no. 3, p.9775. https://doi.org/10.4249/scholarpedia.9775
- Redmon J. and Farhadi A. (2017), "YOLO9000: better, faster, stronger," arXiv preprint. Thtrieu. darkflow. https://github.com/thtrieu/darkflow, 2016.
- 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.
- Ren S., He K., Girshick R. and Sun J. (2015), "Faster r-cnn: Towards real-time object detection with region proposal networks," In Advances in neural information processing systems, pp.91-99.
- Rublee E., Rabaud V., Konolige K. and Bradski G. (2011, November), "ORB: An efficient alternative to SIFT or SURF," In Computer Vision (ICCV), 2011 IEEE international conference, IEEE, pp.2564-2571.
- Szegedy C., Liu W., Jia Y., Sermanet P., Reed S., Anguelov D. and Rabinovich A. (2015), "Going deeper with convolutions," In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.1-9.
- Taghvaeeyan S. and Rajamani R. (2014), "Portable roadside sensors for vehicle counting, classification, and speed measurement," IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 1, pp.73-83. https://doi.org/10.1109/TITS.2013.2273876
- Udacity (2018, April 18), Udacity/self-driving-car, Retrieved September 11, 2018, from https://github.com/udacity/self-driving-car/tree/master/annotations.
- Valk L. (2014), Lego mindstorms Ev3 Discovery Book: A beginner's guide to building and programming robots, No Starch Press.
- Xiao J., Liao L., Hu J., Chen Y. and Hu R. (2015), "Exploiting global redundancy in big surveillance video data for efficient coding," Cluster Computing, vol. 18, no. 2, pp.531-540. https://doi.org/10.1007/s10586-015-0434-z
- Zhang C., Li H., Wang X. and Yang X. (2015), "Cross-scene crowd counting via deep convolutional neural networks," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.833-841.
- Zhou Y., Nejati H., Do T. T., Cheung N. M. and Cheah L. (2016, October), "Image-based vehicle analysis using deep neural network: A systematic study," In Digital Signal Processing (DSP), 2016 IEEE International Conference, IEEE, pp.276-280.