Detection of a Moving Car Based on Invariant Moments

Abstract

This paper presents a proposed technique based on Invariant Moments to detect moving cars in front of Autonomous Cars in order to increase safety and reduce road accidents, thus saving lives, which are one of the most important matters in the context of Autonomous Cars. Object detection from a sequence of images is preparatory step but a crucial mission for computer vision applications in information extraction. In this work, a car database was constructed and used in background subtraction to define Region of Interest, then applying the invariant moments for RoI and dependence on the information of the database that was constructed to detect and recognize the moving car. The technique was tested and the experimental results showed that it could detect the moving objects successfully with detection rate over 87% and precision 97% and FoM 91%.

Modeling Dynamic Background based on Linear Equation

“…The objectives of these cars are reducing risks, problems, and cost that coming from human operation. One of AC major functions is detecting moving car in the front view based on computer vision technologies which help to make the decision for determining the path and avoid accidents [1]. Background modeling and subtraction is the most common detecting technique, while how correctly detecting moving objects is still a challenge [2].…”