基于TOF相机的俯视行人检测

在计算机视觉领域，行人检测是一项艰巨的任务。利用TOF（time-of-flight）相机提供的深度信息进行俯视行人的高精度检测。当TOF相机处于俯视角度时，作为人体的一部分，人的头部具有非常丰富的特征信息，并且能在很长的时间里不被遮挡。鉴于此，提出了一种头部检测与注水算法相结合的俯视行人检测方法。该方法首先利用混合高斯背景模型来寻找图像中的感兴趣区域；然后采用注水算法对候选头部区域进行过滤；最后，结合深度信息等先验条件来确定场景中行人的真实头部。为了验证算法的有效性，分别在两种实时采集的深度图像数据集上进行了实验，与其他算法相比，本文所提出的方法具有更好的性能，能够实现实时、准确的行人检测。

People detection under an overhead time of flight camera

People detection is a challenge task in the field of computer vision. This paper aims to detect the pedestrians with high precision by only using the depth information provided by TOF(time-of-flight) camera. When the TOF camera is overhead, the human head, as a part of the human body, has very rich feature information and it can be unobstructed for a long time. In view of this, a people detection method, combing blob detection with water filling algorithm, is proposed. The proposed method first uses the mixture Gaussian background model to find the region of interest in the image; then it uses the water filling algorithm to filter out the candidate heads; at last, it combines with the prior condition to determine the real heads in the scene. In order to verify the effectiveness of the proposed algorithm, two kinds of real-time depth image datasets were tested. The result turns out that, compared with two other algorithms, the proposed method has better performance, and can achieve real-time and accurate people detection.