A generic deformable model for vehicle recognition
BMVC '95 Proceedings of the 1995 British conference on Machine vision (Vol. 1)
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition Robust Under Translations, Deformations, and Changes in Background
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Trainable System for Object Detection
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Gender Classification with Support Vector Machines
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
On-Road Vehicle Detection: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Template Matching Based on Normalized Cross Correlation With Adaptive Multilevel Winner Update
IEEE Transactions on Image Processing
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This paper presents a new novel approach for automatic vehicle detection from a live video based on texture Gabor features. Vehicle detection is a pivotal part in collision avoidance systems, blind-spot monitoring, and self-guided vehicles. This system uses a low cost camera mounted near the rear-view mirror to obtain the live video. The Gabor filter features have been used to identify the potential vehicles in the frames by using the support vector machine. The initial detection of potential vehicle candidates has been improved by using correlation techniques. A robust detection technique is developed in which the vehicles are detected with accuracy up to 9 % in day light image sequences.