A simplified nonlinear regression method for human height estimation in video surveillance

This paper presents a simple camera calibration method for estimating human height in video surveillance. Given that most cameras for video surveillance are installed in high positions at a slightly tilted angle, it is possible to retain only three calibration parameters in the original camera model, namely the focal length, the tilting angle and the camera height. These parameters can be directly estimated using a nonlinear regression model from the observed head and foot points of a walking human instead of estimating the vanishing line and point in the image, which is extremely sensitive to noise in practice. With only three unknown parameters, the nonlinear regression model can fit data efficiently. The experimental results show that the proposed method can predict the human height with a mean absolute error of only about 1.39 cm from ground truth data.