在特定出入口及場所取得進出人員之人流數據，具有非常高之參考價值；而人數統計技術是影像監視系統方面很重要的課題之一。在近幾年研究中，我們發現遮蔽現象(Occlusion)的問題一直存在。因此本論文提出深度垂直位置圖轉換之方法來偵測目標物的存在，並進行形態學-侵蝕與膨脹之處理，再使用8-連通法(8-Connected Component Labeling)來找出目標物之區域(Region of Interest, ROI)，之後依ROI所找出之目標物區域做閥值的判斷，最後分成進出人流計算和區域內之人流計算兩大部分，之後將所得之實驗結果與人工計數結果做比較，本系統在正常情況下，具有極佳之精確度，並可符合即時(Real-time)之需求。 This paper proposed a bi-directional people-flow counting system on Kinect. It can also be applied to multi-flow corresponded with the demand for the practical application. Firstly, we set the Kinect above the doorway to capture the situation of people-flow. Then this system detects people in the covering area using the depth image information from Kinect system. And we do the morphological processing like erosion to the object and use the 8-connected component to find the region of interest (ROI) which often performed on using a mapping-based detection approach. After these previous steps, this system set a detected line and let people go through it. Therefore, we can get people number of the experimental result. For the multi-flow case, it will cause the occlusion problem, so we could apply the depth information to distinguish the target on occlusion problem. Final, we compare the experimental results with the manual count results and other research. Under normal circumstances, our system provides not only almost 100% for bi-directional counting but also correspond with the demand for real-time.