3D-Net: Monocular 3D object recognition for traffic monitoring

Abstract

Machine Learning has played a major role in various applications including Autonomous Vehicles and Intelligent Transportation Systems. Utilizing a deep convolutional neural network, the article introduces a zero-calibration 3D Object recognition and tracking system for traffic monitoring. The model can accurately work on urban traffic cameras, regardless of their technical specification (i.e. resolution, lens, the field of view) and positioning (location, height, angle). For the first time, we introduce a novel satellite-ground inverse perspective mapping technique, which requires no camera calibrations and only needs the GPS position of the camera. This leads to an accurate environmental modeling solution that is capable of estimating road users’ 3D bonding boxes, speed, and trajectory using a monocular camera. We have also contributed to a hierarchical activity/traffic modeling solution using short- and long-term Spatio-temporal video analysis to understand the heatmap of the traffic flow, bottlenecks, and high-risk zones. The experiments are conducted on four datasets: MIO-TCD, UA-DETRAC, GRAM-RTM, and Leeds-Dataset including various use cases and traffic scenarios.