PEDESTRIAN TRAFFIC SURVEY BY TRACKING HEADS

Abstract

 Currently, in many cases of pedestrian traffic measurement, pedestrians are counted by field surveying or a visual check of motion images. Accordingly, there are many problems including error counts caused by human error and the risk of heatstroke caused by outdoor measurement for long hours. In addition, in the environment where unspecified large numbers of people come and go, a problem of a decrease in pedestrian measurement precision due to occlusion has been revealed, and there is no solution established for this problem yet.

 In this study, a practical method for pedestrian traffic measurement was derived by verifying the technology of person recognition using deep learning capable of treating occlusion, and clarifying its problems. As a result, it was found that even if most of the person region is hidden, the number of pedestrians can be measured with high precision by applying the proposed method.