Enhanced Vehicle Detection and Tracking System for Nighttime

Abstract

The objective of this paper is to assist the driver during nighttime. Many papers were published which concerned with detection of vehicle during daytime. We propose a method which detects the vehicle during nighttime since it is problematic for human to analyze the shape of vehicles in nighttime due the limits of human vision. This project endeavors to implementation of vehicle detection based on vehicle taillight and headlight using technique of blobs detection by Center Surround Extremas (CenSurE). Blobs are the bright areas of the pixels of headlight and taillight. First stage is to extract blobs from region of interest by applying multiple Laplacian of Gaussian (LoG) operator which derive the response by manipulating variance between surrounding of blob and luminance of blob which are grabbed on road scene images. Compared to the automatic thresholding technique, Laplacian of Gaussian operator provides more robustness and adaptability to work under different illuminated conditions. Then vehicle lights which are extracted from the first stage are clustered based on the connected—component analysis procedure, to confirm that it is vehicle or not. If vehicle is detected, then tracking of vehicle is done on the basis of the connected components using bounding box and different tracking parameters.