An improved corner detection algorithm used in video statistics

Abstract

In order to address the difficult problem to determine the number of populations, this paper improves the algorithm based on the Harris point detection algorithm, and the number of people is returned through the first-order linear regression model. First of all, according to the shortcomings of Harris corner algorithm in population statistics, an adaptive gray difference idea is proposed, and the concept of integral image is introduced to overcome its defects in noise immunity and real-time operation. Secondly, in view of the large error generated in the process of population statistics in the first-order static model, a dynamic linear model regression method is proposed. In this method, it is believed that there is certain proportionality coefficient between each frame of corner points and the number of people with the change of time, and this coefficient has certain correlation with the angle points in the previous frame and current frame. At the same time, in order to eliminate the number of redundant corners generated in the corner statistics process, the frame difference method is used to filter the stationary point. Finally, the number of people is returned through first-order linear model.