V-DaT: A Robust method for Vehicle Detection and Tracking

Latha Anuj , Et Al

doi:10.17762/turcomat.v12i2.2092

Abstract

Vision-based traffic surveillance has been one of the most promising fields for improvement and research. Still, many challenging problems remain unsolved, such as addressing vehicle occlusions and reducing false detection. In this work, a method for vehicle detection and tracking is proposed. The proposed model considers background subtraction concept for moving vehicle detection but unlike conventional approaches, here numerous algorithmic optimization approaches have been applied such as multi-directional filtering and fusion based background subtraction, thresholding, directional filtering and morphological operations for moving vehicle detection. In addition, blob analysis and adaptive bounding box is used for Detection and Tracking. The Performance of Proposed work is measured on Standard Dataset and results are encouraging.

Highlights

Traffic monitoring and control mechanism are employed by different socioeconomic and administrative entities including private/public companies, government’s administrative agencies to enable efficient and safe traffic navigation and control.Certain static camera setup supervising certain specific object or scene is usually stated as asurveillance system
The predominant purpose of moving object detection and segmentation is to retrieve the significant information about the moving vehicle from certain video sequences that as a result enables tracking and further classification and decision processes
Vehicle detection is vital in major video based applications such as video surveillance, vehicle tracking, and vehicle tracking under occlusion, and pattern recognition and classification

Summary

Introduction

Traffic monitoring and control mechanism are employed by different socioeconomic and administrative entities including private/public companies, government’s administrative agencies to enable efficient and safe traffic navigation and control. Authors applied moving object segmentation and blob analysis to perform moving vehicle detection and tracking At first, they performed blob analysis, based on which they extracted significant features. Li et al [13] developed a vision-based approach to perform forward vehicle detection and tracking At first, they applied histogram method to perform shadow segmentation beneath vehicle region. Lee et al, [15] applied the concept of tracking feature points to perform real-time vehicle detection and lane change detection Authors stated their approach as switch- independent which was not depending on the illumination conditions. The feature clustering scheme with heuristic filtering based blob analysis makes this proposed model more efficient and precise for accurate moving vehicle detection. 2) have been converted into gray color image (Figure 2), which is followed by filtering and vehicle segmentation process

Background

Conclusion