Estimation of Traffic Occupancy using Image Segmentation

M U Farooq,A Ahmed,M B Nawaz,S M Khan

doi:10.48084/etasr.4218

Abstract

Increased traffic flow results in high road occupancy. Traffic road occupancy is often used as a parameter for the prediction of traffic conditions by traffic engineers. Although traffic monitoring systems are based on a large number of technologies, challenges are still present. Most of the methods work efficiently for free-flow traffic but not in heavy congestion. Image processing techniques are more effective than other methods, as they are based on loop sensors and detectors to monitor road traffic. A huge number of image frames are processed in image processing hence there is a need for a more efficient and low-cost image processing technique for accurate vehicle detection. In this paper, a novel approach is adopted to calculate road occupancy. The proposed framework has robust performance under road conjunction and diverse environmental conditions. A combination of image segmentation threshold technique and shadow removal technique is used. The study comprised of segmenting 1056 images extracted from recorded videos. The obtained results by image segmentation were compared with traffic road occupancy calculated manually using Autocad. A final percentage difference of 8.7 was observed.

Highlights

Precise and accurate calculation of basic traffic flow parameters is a major step towards the successful planning and management of a traffic system
Real time traffic flow can be used to enhance the capacity of a traffic system
Obtaining traffic road occupancy for heterogeneous traffic is a difficult task due to the variety of traffic modes in the traffic composition, the ranging in area and size, and little to no lane discipline

Summary

Introduction

Precise and accurate calculation of basic traffic flow parameters is a major step towards the successful planning and management of a traffic system. Real time traffic flow can be used to enhance the capacity of a traffic system. Traffic road occupancy is often used to determine traffic congestion and related characteristics. It is the ratio of the area occupied by vehicles to the total area of the road in a traffic scenario. Road occupancy has been a subject of interest for traffic management and planners over time [1, 2]. Image processing has been used in a number of ways to obtain real time traffic in terms of both model flow and density. Occlusion affects accuracy and real time traffic management systems cannot be adopted [3,4,5]

Methods

Results

Conclusion