隨著大規模部署於城市街道和公共開放區域之監控攝影機，媒體流和存檔監控影像已成為有用 的資源，可用於追蹤可疑人員或車輛；在室內小區域或攝影機密集的地區可很容易地追蹤對象，但 當攝影機部署不密集時，則無法於多個攝影機之間進行無縫追蹤。我們將以影像內容檢索作為基礎， 使用者操作指定查詢一個特定的目標圖像，根據相似度從不同攝影機的監控影像中找出優先排序圖 像集合。地理信息和時間編碼會標記於監控影像中，因此相似性度量可根據物件外觀與時空限制來 計算，特別是藉由跨攝影機之間位置，將檢索圖像分類和標記，因此使用者可以輕鬆地獲得廣域中 車輛之移動軌跡。

With the large-scale deployment of surveillance cameras along city streets and public open areas, the streaming and archived surveillance videos have become useful resources for tracking suspicious persons or vehicles. Unlike video surveillance for small indoor areas where objects can be easily tracked across cameras based on overlapping regions, cameras deployed in wide area are not dense enough for seamless tracking across multiple cameras. The human operator can specify an image containing the desired target as the query, and the system returns a ranked set of images from other cameras according to the similarity measure. The similarity measure is based on object appearance as well as the spatial-temporal constraints obtained from the provided geographic information and time stamps encoded on the images in the archive. In particular, the retrieved images are clustered and represented by paths where each path contains consistent video frames across multiple cameras.