Latest trend of variation of EDGE detection and object detection with pixel level variation and their comparison algorithms

Bhubneshwar Sharma,Parul Mahajan

doi:10.7439/ijasr.v1i9.2461

Abstract

The frequent traffic jams at major junctions call for an efficient traffic management system in place. The resulting wastage of time and increase in pollution levels can be eliminated on a city-wide scale by these systems. The image sequences from a camera are analyzed using various edge detection and object counting methods to obtain the most efficient technique. Subsequently, the number of vehicles at the intersection is evaluated and traffic is efficiently managed.

Highlights

The application of image processing and computer vision techniques to the analysis of video sequences of traffic flow offers considerable improvements over the existing methods of traffic data collection and road traffic monitoring
The background is replaced by the current frame only at regions with no motion detected; where the difference between the current and the previous frames is smaller than a threshold
Selective updating can be performed in a more robust averaging form, where the stationary regions of the background are replaced by the average of the current frame and the previous background

Summary

Introduction

The application of image processing and computer vision techniques to the analysis of video sequences of traffic flow offers considerable improvements over the existing methods of traffic data collection and road traffic monitoring. Image processing offer a relatively low installation cost with little traffic disruption during maintenance. They provide wide area monitoring allowing analysis of traffic flows and turning movements, speed measurement, multiple-point vehicle counts, vehicle classification and highway state assessment (e.g. congestion or incident detection). This class relates the detection of the lane with the changing intensity distribution along the region of a lane This class considers just the changes in the gray-scale values within an image sequence. The (R, G, B) value defines the feature vector and the classification can be performed directly on the (R, G, B) scatter diagram of the image

Automatic lane Finding

Object Detection

Conclusion