Automatic Vehicles Detection, Classification and Counting Techniques / Survey

Mustafa Najm,Yossra Hussein Ali

doi:10.24996/ijs.2020.61.7.30

Abstract

Vehicle detection (VD) plays a very essential role in Intelligent Transportation Systems (ITS) that have been intensively studied within the past years. The need for intelligent facilities expanded because the total number of vehicles is increasing rapidly in urban zones. Trafï¬c monitoring is an important element in the intelligent transportation system, which involves the detection, classification, tracking, and counting of vehicles. One of the key advantages of traffic video detection is that it provides traffic supervisors with the means to decrease congestion and improve highway planning. Vehicle detection in videos combines image processing in real-time with computerized pattern recognition in flexible stages. The real-time processing is very critical to keep the appropriate functionality of automated or continuously working systems. VD in road traffics has numerous applications in the transportation engineering field. In this review, different automated VD systems have been surveyed, with a focus on systems where the rectilinear stationary camera is positioned above intersections in the road rather than being mounted on the vehicle. Generally, three steps are utilized to acquire traffic condition information, including background subtraction (BS), vehicle detection and vehicle counting. First, we illustrate the concept of vehicle detection and discuss background subtraction for acquiring only moving objects. Then a variety of algorithms and techniques developed to detect vehicles are discussed beside illustrating their advantages and limitations. Finally, some limitations shared between the systems are demonstrated, such as the definition of ROI, focusing on only one aspect of detection, and the variation of accuracy with quality of videos. At the point when one can detect and classify vehicles, then it is probable to more improve the flow of the traffic and even give enormous information that can be valuable for many applications in the future.

Highlights

The importance of efficient vehicle detection (VD) is increasing with the expansion of road networks and number of vehicles
The results showed the probability of the advanced method to be used in any automobile zone used for the park
Detecting a vehicle accurately in real-time videos is one of the major research areas in the arena of computer vision; the mentioned systems are capable to be employed in different applications and environments

Summary

Introduction

The importance of efficient vehicle detection (VD) is increasing with the expansion of road networks and number of vehicles. The experimental results revealed the good performance of the system and its accuracy in actual situations with general problems present in the highway, such as different illumination, camera vibration, conditions of weather, and shadows Enhancements such as utilizing GPU programming or increasing other features depend on the texture of the vehicles classifying portion, which is proposed to enhance the system run time and performance. Zhuang et al [16] suggested an algorithm for vehicle detection in real-time, which depends on the enhanced Haar-like features and gathering a cascade of classifiers with motion detection It adapts a background extractor based on visual features, supplemented by a morphological process, to acquire a foreground. For each methods reviewed from related works, many strength and weakness points are demonstrated in Table-1

Method

Findings

Conclusions