Core Concepts
Optical flow technique improves moving object detection and tracking for autonomous vehicles.
Abstract
The article discusses the importance of accurate velocity estimation and trajectory tracking for autonomous vehicles. It introduces a novel Detection and Tracking of Moving Objects (DATMO) technique based on optical flow, which outperforms existing methods in terms of accuracy and processing time. The study evaluates the proposed technique using synthetic and real-world data, demonstrating its superiority.
The content is structured as follows:
Introduction to the challenges in perception for AVs.
Comparison of existing DATMO techniques.
Proposal of a novel DATMO technique based on optical flow.
Detailed explanation of the proposed method's processes.
Performance evaluation using synthetic and real-world data.
Comparison with state-of-the-art methods.
Sensitivity analysis of estimation errors to target vehicle configurations.
Discussion on the results and implications.
Stats
Recent point cloud based solutions often use Iterative Closest Point (ICP) techniques.
The proposed DATMO technique is computationally efficient and accurate.
The study evaluates the proposed technique using synthetic and real-world data.
Quotes
"Accurate velocity estimation of surrounding moving objects and their trajectories are critical elements of perception systems in Automated/Autonomous Vehicles (AVs) with a direct impact on their safety."
"The proposed DATMO technique is inspired by optical flow algorithm."