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.
