Detection Method of Tourist Flow in Scenic Spots based on Kalman Filter Prediction

Abstract

The tourism industry has developed rapidly, but it is always limited by the environmental carrying capacity and cannot receive too many tourists at the same time. Therefore, it is very necessary to limit the number of tourists visiting at the same time based on traffic detection. To this end, the tourist scenic spot (TSS) traffic statistics system was designed. The system performed graying, binarization, image denoising, and morphological processing on the image. The pre-processed image used the background difference method based on mixed Gaussian background modeling to detect moving objects. The improved Hough transform circle detection method was used to identify the head target, and the Kalman filter (KF) was used to complete the target tracking. KF could predict the target trajectory accurately, and the improved Hough transform circle detection method could recognize the head under occlusion. The maximum missed detection rate of the statistical system was 3.2\%, the minimum is 0, and the overall detection accuracy was the highest. The error rate of inbound passenger flow was 4.10\%, and the error rate of outbound passenger flow was 3.0\%. Using this system can control the tourist flow (TF) in the scenic spot and avoid safety accidents due to excessive passenger flow. And it is conducive to the sustainable development of the scenic spot.