本研究提出一個針對固定式道路監視畫面之分析工具，用以協助解決車輛影像交疊問題，並提升車流評估及車輛分類準確度。本論文主要分為兩個部份，第一部份為模型訓練機制，經由搜集之交通場景及車輛相關資訊，分析其統計特性，取得目標道路車流方向及出現之機車、汽車、公車等各類車輛大小資訊，接著以自動化的方式建立交通場景模型及代表車輛之隱式型態模式 (ISM)。值得注意的是，此自適應機制可以大幅減少模型建置的人力需求。第二部份結合了訓練完成的ISM，對可能發生車輛影像交疊的部份進行辨識。實驗結果顯示了這個機制確實能夠適應不同的交通場景，並且有效地解決道路監視器畫面中車輛影像交疊的問題。This research presents a framework of analyzing the traffic information in the surveillance videos from the static roadside cameras to assist resolving the vehicle occlusion problem for more accurate traffic flow estimation and vehicle classification. The proposed scheme consists of two main parts. The first part is a model training mechanism, in which the traffic and vehicle information will be collected and their statistics are employed to automatically establish the model of the scene and the implicit shape model of vehicles. It should be noted that the proposed self-training mechanism can reduce a great deal of human efforts. The second part adopts the established implicit shape model, which is a highly flexible learned representation, for vehicle recognition when possible occlusions of vehicles are detected. Experimental results demonstrate that the proposed scheme can deal with the scenes with different characteristics and the occlusion problem in traffic surveillance videos can be reasonably resolved.
