影片中的人物辨識很具有挑戰性，因為不同的視角、光照的變化、行人偵測與錯誤的前景分割都會影響結果。為了提高影片人物辨識性能，在本文中我們加入行人高度資訊與時間訊息，以提高辨識系統中行人偵測率。
我們所提出的方法包括以下三步驟：首先採用前景偵測去取得前景區域，並取前幾張畫面中偵測行人，以獲得的行人資訊來建立影片中行人位置與大小關係的模型。其次，我們利用前景配合滑動窗口進行搜尋比對，使用相對應區塊中的色彩與紋理特徵，以及滑動窗口內的行人機率，來提高行人偵測正確率。最後我們比較這些特徵、計算其相似分數，並且根據畫面連續性做分數調整。
實驗結果顯示：根據行人高度資訊與畫面連續性進行改進後，行人辨識效能將顯著的上升。

Person re-identification from video is very challenging due to the problems arising from different viewpoints, illumination variations, background subtraction errors or human detection etc. In order to improve the performance of person re-identification in surveillance video, we propose to incorporate the human height information and temporal consistency to enhance the human detection in the person re-identification system.
The proposed system consists of the following steps: First, we employ background subtraction to obtain the foreground regions and apply the human detector to some beginning frames to estimate the parameters for human height projection onto images. Thus, we can establish the relationship between image positions and the sizes of sliding windows for human detection. Second, we apply the sliding window search on the foreground region by computing the associated color and texture histograms. Thus, the probability of human detection with the SLBP features is computed to improve the human detection accuracy. Finally, we compare the features and compute the similarity score with adjustment based on temporal consistency.
Experimental results show superior performance on person re-identification by applying the proposed algorithm that utilizes human height information and temporal consistency to some publicly available datasets.

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