Physical Parameters Estimation Using Roadside Monocular Vision

Roadside sensing is an important part of intelligent traffic management systems (ITMSs) for collecting and processing information. In order to better assess and maintain the stability and safety of objects in traffic scenes, all types of basic information are required. This paper proposes a monocular vision-based object parameter measurement and geolocation method to address the problems of high cost and limited information dimension of traditional roadside sensors. Object detection and geometric transformation mapping are combined to achieve efficient estimation of key physical parameters with input of monocular images, and global navigation satellite system (GNSS) information is further incorporated to obtain geolocation of the target. In the method, after the key target is recognized by the neural network-based object detection algorithm, the pixel-level 2D image information is mapped to a series of 3D spaces based on the construction of a geometric model, which leads to further computation of various physical parameters, realizing multi-parameter estimation under one method. The method overcomes the dependence on fixed environments or known references and is highly applicable to non-cooperative scenes. The effectiveness of the method is shown via the experiments in multiple real scenes.