擁擠環境中的人群偵測與追蹤

Abstract

以人工監看方式來進行人潮的管理，不僅費時費力，並且容易發生疏忽。以電腦分析影像來進行自動化的人潮計數與追蹤，除可節省人力的耗損外，更可降低因人為疏忽所造成的危險。但在人潮擁擠的環境下，一般所使用45度俯角的攝影方式，會因人在行走交錯時互相遮蔽的效應，而造成人群計數上的困難。為了降低遮蔽效應所造成的影響，本論文採用垂直向下的攝影視角，發展一套可在擁擠環境中進行人群計數與追蹤的方法。 此方法以人頭邊緣灰階梯度方向的放射趨勢為基礎，利用群聚的方式來偵測影像中人頭的位置以及數量，並利用色彩及路徑等特徵，在連續的影像中進行以偵測為基礎的多人移動追蹤。經由實驗發現，本論文的方法不論是在人潮稀疏或擁擠的狀況下，均可達到80%以上的正確率。顯示此方法不受遮蔽效應所影響，並能適用於人潮擁擠的環境下。我們將此方法實作為一自動化的人潮監控系統，使其能夠應用在實際環境中。

Human monitoring and controlling the crowded situation is not only tedious work but also easy to get mistakes. Automatic head counting and tracking can save the manpower and reduce the chance of human negligence. Because of the occlusion between people, it is difficulty to count human by the frontal view. In order to reduce the occlusion effect, we using the overhead view of people to develop a human counting and tracking method in crowded scene. Based on the radiation of grey-level gradient direction along the human head contour, the method detects human head position in image by clustering. And track multiple people by color and trajectory analysis from the detection results in image sequence. The experimental results presented no matter under sparse or crowed situation, our method can achieve above 80% correction rate. It presents our method doesn’t affected by occlusion effect and can be used in crowded scene. We implement our method as an automatic surveillance system and apply it in a real world.