3D Object Detection and Vehicle Classification based on LiDAR Point Clouds for Real-time Traffic Flow Monitoring

Research Team

Lead Researchers:

• Xunfei Jiang, Computer Science
• Bingbing Li, Manufacturing Systems and Engineering
• Xudong Jia, Civil Engineering

Collaborators:

Student Team:

• Chen Li*, M.S. student in Computer Science at Columbia University 
• Tongzi (Peter) Wu, Research Assistant at ARCS 
• Alexander Rose*, M.S. student in Software Engineering at CSUN 
• Ruitao Wu, B.S. student in Computer Science at CSUN (ARCS Fellow) 
• Icess Nisce*, B.S. student in Computer Science at CSUN (ARCS Fellow) 
• Brian Uribe*, B.S. student in Computer Science at CSUN 
• Javier Carranza*, B.S. student in Computer Science at CSUN 
• Alondra Gonzalez*, B.S. student in Computer Science at CSUN 
• Matthew Davis*, B.S. student in Computer Science at CSUN 
• Andres Mercado*, B.S. student in Computer Science at CSUN 
• Ziaur Chowdhury, B.S. student in Computer Science at CSUN 
• Gustavo Velasquez Sanchez, B.S. in Computer Science at CSUN 
• Xin Gao, B.S. in Computer Science at CSUN 
• Cesar Villa, B.S. in Computer Science at CSUN 

Funding

• Funding Organization:
• Funding Program:

Abstract

Vehicle detection plays an important role in analyzing traffic flow data for efficient planning in intelligent transportation. Machine Learning technology has been increasingly used for vehicle detection in both 2D real-time traffic flow video and 3D point clouds. Adverse weather conditions, such as fog, rain, snow, extreme wind, and other conditions prove to be challenging for 2D vehicle detection. 3D LiDAR point clouds can be more resistant to adverse weather conditions. Most of the existing research on 3D vehicle detection are used for autonomous vehicle driving with the LiDAR cameras are deployed on vehicles. There is a lack of research on real-time vehicle detection for intelligent transportation with LiDAR cameras deployed by highway/freeway. In this project, we propose to build a system that collects real-time traffic flow data through 3D LiDAR cameras, processes the 3D point cloud data for vehicle detection and classification, and provides a web-based service with vehicles detected and classified in real-time traffic flow and data visualization for statistical traffic flow data.

Motivation/Research Problem

Object detection includes the parametrization of a bounding box containing the recognized and classified object. Most research in that area has focused on two-dimensional (2D) object detection based on widely available Red-Green-Blue (RGB) or grayscale images. However, this completely leaves out the third dimension and only partly imitates the human visual system. In fact, many modern applications like robot assistants or autonomous vehicles are highly dependent on depth and surface data to securely navigate in three-dimensional (3D) environments through LiDAR cameras. With the rising availability of mobile 3D sensors like RGB-Depth (RGB-D) cameras and LiDAR (light detection and ranging) sensors, more depth data can be captured and processed. This allowed great progress in the field of 3D object detection (3DOD) and brought forth a variety of heterogeneous methods and solutions used for this task.