影像辨識可以應用在許多ITS的領域中。透過自動化的車流計數，可以更有效地掌握某區域的交通情況。藉由現有的影像辨識model可以輕易地提取出影片中每個frame中物體的座標。將提取出的物體座標過濾後即可得到所需要的車輛座標。然而要達到車輛計數的功能，需要使系統明白每個frame中車輛之間的關係。將辨識model輸出的座標交由Tracking Algorithm處理雖可達到計數的效果，但若遇到辨識model中途辨識失敗則會造成錯誤的追蹤而導致計數錯誤。本篇論文提出的系統架構主要包含三個區塊，負責車輛辨識的Detector，儲存車輛座標的Buffer，最後是負責車輛計算的Counter。本系統只需利用簡單的距離運算即可達到車輛計數的功能。此外經由增加檢查點使系統能夠容忍短暫的ＹＯＬＯ辨識失敗且不影響車輛計數。最後利用學校兩個出口的影片來驗證與分析本系統的正確性與整體效率。

Image recognition can be applied in many applications of Intelligent Transportation System (ITS). Through automated traffic flow counting, the traffic information can be presented effectively for a given area. After the existing image recognition model process the monitoring video, the coordinates of objects in each frame can be easily extracted. The extracted object coordinates are then filtered to obtain the required vehicle coordinates. To achieve the function of vehicle counting, it is necessary to identify the relationship of vehicles in different frames, i.e., whether or not they represent the same vehicle. Although the vehicle counting can be achieved by using the tracking algorithm, a short period of recognition failure may cause wrong tracking, which will lead to incorrect traffic counting. In this thesis, we propose a system that utilizes the YOLO framework for traffic flow counting. The system architecture consists of three blocks, including the Detector that generates the bounding box of vehicles, the Buffer which stores coordinates of vehicles, and the Counter which is responsible for vehicle counting. The proposed system requires only to utilize simple distance calculations to achieve the purpose of vehicle counting. In addition, by adding checkpoints, the system is able to alleviate the consequence of false detection. The videos from different locations and angles are used to verify and analyze the correctness and overall efficiency of the proposed system, and the results indicate that our system achieves high counting accuracy under the environment with sufficient ambient light.

[1] Herbert Bay, Tinne Tuytelaars, and Luc Van Gool. Surf: Speeded up robust features. In European conference on computer vision, pages 404–417. Springer, 2006.
[2] Corinna Cortes and Vladimir Vapnik. Support-vector networks. Machine learning, 20(3):273–297, 1995.
[3] M Cremer and Markos Papageorgiou. Parameter identification for a traffic flow model. Automatica, 17(6):837–843, 1981.
[4] Navneet Dalal and Bill Triggs. Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 1, pages 886–893. IEEE, 2005.
[5] George Dimitrakopoulos and Panagiotis Demestichas. Intelligent transportation sys- tems. IEEE Vehicular Technology Magazine, 5(1):77–84, 2010.
[6] Janusz Gajda, Ryszard Sroka, Marek Stencel, Andrzej Wajda, and Tadeusz Zeglen. A vehicle classification based on inductive loop detectors. In Instrumentation and Mea- surement Technology Conference, 2001. IMTC 2001. Proceedings of the 18th IEEE, volume 1, pages 460–464. IEEE, 2001.
[7] Ross Girshick. Fast r-cnn. arXiv preprint arXiv:1504.08083, 2015.
[8] Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Region-based con- volutional networks for accurate object detection and segmentation. IEEE transac- tions on pattern analysis and machine intelligence, 38(1):142–158, 2016.
[9] Sepp Hochreiter and Ju ̈rgen Schmidhuber. Long short-term memory. Neural compu- tation, 9(8):1735–1780, 1997.
32[10] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105, 2012.
[11] Rainer Lienhart and Jochen Maydt. An extended set of haar-like features for rapid object detection. In Image Processing. 2002. Proceedings. 2002 International Con- ference on, volume 1, pages I–I. IEEE, 2002.
[12] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll ́ar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740–755. Springer, 2014.
[13] RA Lotufo, AD Morgan, and AS Johnson. Automatic number-plate recognition. In Image Analysis for Transport Applications, IEE Colloquium on, pages 6–1. IET, 1990.
[14] David G Lowe. Object recognition from local scale-invariant features. In Computer vision, 1999. The proceedings of the seventh IEEE international conference on, vol- ume 2, pages 1150–1157. Ieee, 1999.
[15] Donald M Merhar. Piezoelectric vehicle impact sensor, October 31 1972. US Patent 3,701,903.
[16] Kenneth H Molloy, John P Ward, and Victor Mark Benson. Traffic signal control system, July 4 1972. US Patent 3,675,196.
[17] Kenichi Morinaga. Car navigation system, May 16 1995. US Patent 5,416,478.
33
[18] Guanghan Ning, Zhi Zhang, Chen Huang, Xiaobo Ren, Haohong Wang, Canhui Cai, and Zhihai He. Spatially supervised recurrent convolutional neural networks for visual object tracking. In Circuits and Systems (ISCAS), 2017 IEEE International Symposium on, pages 1–4. IEEE, 2017.
[19] Samer A Rajab and Hazem H Refai. A single element piezoelectric sensor for ve- hicle classification using the ratio of track width to length. In Intelligent Vehicles Symposium Proceedings, 2014 IEEE, pages 1463–1468. IEEE, 2014.
[20] Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 779–788, 2016.
[21] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems, pages 91–99, 2015.
[22] Dominik Scherer, Andreas Mu ̈ller, and Sven Behnke. Evaluation of pooling operations in convolutional architectures for object recognition. In International conference on artificial neural networks, pages 92–101. Springer, 2010.