A study on identification of two-wheeled vehicle using object detection and human pose estimation in road space

Abstract

General object recognition can be used to recognize traffic participants by applying it to images of road spaces. However, general object recognition, which identifies objects based on their appearance, has a problem with the detection and recognition of two-wheeled vehicles. In this paper, we propose a method to identify two-wheeled vehicles by applying general object recognition and human pose estimation to road videos, focusing not only on the vehicles themselves but also on the drivers of the twowheeled vehicles. The proposed method first applies general object recognition and human pose estimation to road video. Next, we track the obtained posture data and obtain features that represent the motion of the two-wheeled vehicle driver. By constructing a discriminator using the obtained features as input, a two-wheeled vehicle driver is extracted from the results of human pose estimation. Next, the extracted two-wheeled vehicle drivers are discriminated into bicycles and motorcycles using another discriminator. By integrating the identification results based on human pose estimation with the results of general object recognition, it is possible to identify two-wheeled vehicles that could not be recognized by general object recognition and thus to improve the accuracy of the system. In the last part of this paper, we confirm the effectiveness of the proposed method through experiments using actual road videos.