A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Robust Real-Time Face Detection
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Challenge: peers on wheels - a road to new traffic information systems
Proceedings of the 13th annual ACM international conference on Mobile computing and networking
A cascade of boosted generative and discriminative classifiers for vehicle detection
EURASIP Journal on Advances in Signal Processing
Evaluation of VANET-based advanced intelligent transportation systems
Proceedings of the sixth ACM international workshop on VehiculAr InterNETworking
An intervehicular communication architecture for safety and entertainment
IEEE Transactions on Intelligent Transportation Systems
A general active-learning framework for on-road vehicle recognition and tracking
IEEE Transactions on Intelligent Transportation Systems
Vehicle Detection Using Partial Least Squares
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reliable moving vehicle detection based on the filtering of swinging tree leaves and raindrops
Journal of Visual Communication and Image Representation
On-road vehicle detection using evolutionary Gabor filter optimization
IEEE Transactions on Intelligent Transportation Systems
Fast human action classification and VOI localization with enhanced sparse coding
Journal of Visual Communication and Image Representation
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Image-based vehicle detection has received increasing attention in recent years in the framework of advanced driver assistance systems. However, the variability of vehicles in size, color, shape, etc. poses an enormous challenge, especially for the vehicle verification task. Histograms of Oriented Gradients (HOGs) have successfully been applied to image-based verification of objects. However, these descriptors are computationally demanding and are not affordable for real-time on-road vehicle detection. In this paper, less-demanding HOG descriptors are proposed and evaluated that significantly lighten the computation by exploiting the a priori known vehicle appearance. The proposed descriptors are evaluated on a large, public database and the experiments disclose that the computation times are reduced in a factor of more than 5, thus rendering HOG-based real-time vehicle detection affordable, while achieving detection rates of over 96%.