本研究提出創新於開發個人用戶的行車視訊紀錄器嵌入式裝置使其能主動偵測路肩空位停車格的影像識別功能，並能在於主動識別戶外停車格空位時附加上當前GPS定位座標與目標影像即時傳送無線網路封包給後端的CAAS(Compute as a Service, CAAS)雲端平台接收做運算與儲存，雲端內的即時車位資料庫可提供大眾戶外區域的空停車位資訊;於正常行車時，行車視訊偵測器使用多層級Adaboost訓練的分類器核心進行路況畫面偵測以提供移動視訊感測資訊，而藉由雲端查詢系統設計促使使用者協助回饋正樣本機制來收集受監督的多元化正樣本於Adaboost雲端自動訓練機模組的樣本來源，經新訓練產生的分類器偵測率將可因正樣本回饋數量與種類增加而持續提高，並且於辨識技術上提出了雙區塊尺度雙擊檢測方法(Bi-Block size Double Hits，BBDH)來改善Adaboost分類器易有的誤檢問題。整體雲端回饋訓練架構結合圖訊識別與移動感測技術提供了一套便利駕駛人且節能省碳的戶外停車位查詢資訊平台的LBS創新服務方案，貢獻於有效解決擁擠都市區找尋停車位一位難求的問題，並同時節省駕駛油耗與降低交通碳排放量;BBDH有效提升視訊偵測器各種分類器組合的偵測率3~12 %，實驗中最佳BBDH分類器組合的偵測率可達88%，並且保有每秒20帧的高速運算速度，快速運算的檢測方法於嵌入式系統的即時運算處理有相對高的重要性，因此BBDH檢測方法正適合嵌入式系統環境下即時運算的應用。

In this thesis, an innovation for detecting the nonoccupied car parking space proactively on the roadside based on the vehicle video recorder was proposed. The vehicle video recorder was embedded an image recognition function to detect the parking space on the road side while the vehicle is moving along the road. When a nonoccupied parking space is detected, the image and its GPS information are sent to the backend system via wireless communication system for updating the available parking space data on the database. To improve the recognition rate of the vehicle video recorder, the classifier on the backend is retrained periodically based on the received samples from the end users, and then using the retraining result to update the classifier on the vehicle. To encourage the user feedback the positive parking space information to the system for accumulating the training samples, the system is operated with membership.In this research, a bi-block size double hits (BBDH) was proposed. To increase the probability for finding the objects, and resulting to reduce the processing time per frame. The classifier used is a combination of Adaboost and the probabilistic boosting tree algorithms to improve the system’s recognition rate. According to the experimental results shown, the proposed method can improve the detection rate by 3~12% compared to the existed methods. The maximum recognition rate could reach 88% under the processing rate is 20 frames per second. The figures had shown that the proposed system is suitable for real applications.

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