On-Road Vehicle Detection: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
A cascade of boosted generative and discriminative classifiers for vehicle detection
EURASIP Journal on Advances in Signal Processing
An enhanced memetic differential evolution in filter design for defect detection in paper production
Evolutionary Computation
A Memetic Differential Evolution in Filter Design for Defect Detection in Paper Production
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
An Analysis of Gabor Detection
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Critical motion detection of nearby moving vehicles in a vision-based driver-assistance system
IEEE Transactions on Intelligent Transportation Systems
Dynamic Tracking System through PSO and Parzen Particle Filter
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
Evolving a fuzzy controller for a car racing competition
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
A general active-learning framework for on-road vehicle recognition and tracking
IEEE Transactions on Intelligent Transportation Systems
A channel awareness vehicle detector
IEEE Transactions on Intelligent Transportation Systems
Detection of multiple preceding cars in busy traffic using taillights
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part II
Sensor fusion based obstacle detection/classification for active pedestrian protection system
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
Vanishing point and gabor feature based multi-resolution on-road vehicle detection
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Classification of vehicle type and make by combined features and random subspace ensemble
International Journal of Computational Vision and Robotics
Detection and tracking of underwater object based on forward-scan sonar
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part I
Real-time vehicle detection using equi-height mosaicking image
Proceedings of the 2013 Research in Adaptive and Convergent Systems
Image-based on-road vehicle detection using cost-effective Histograms of Oriented Gradients
Journal of Visual Communication and Image Representation
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Robust and reliable vehicle detection from images acquired by a moving vehicle is an important problem with numerous applications including driver assistance systems and self-guided vehicles. Our focus in this paper is on improving the performance of on-road vehicle detection by employing a set of Gabor filters specifically optimized for the task of vehicle detection. This is essentially a kind of feature selection, a critical issue when designing any pattern classification system. Specifically, we propose a systematic and general evolutionary Gabor filter optimization (EGFO) approach for optimizing the parameters of a set of Gabor filters in the context of vehicle detection. The objective is to build a set of filters that are capable of responding stronger to features present in vehicles than to nonvehicles, therefore improving class discrimination. The EGFO approach unifies filter design with filter selection by integrating genetic algorithms (GAs) with an incremental clustering approach. Filter design is performed using GAs, a global optimization approach that encodes the Gabor filter parameters in a chromosome and uses genetic operators to optimize them. Filter selection is performed by grouping filters having similar characteristics in the parameter space using an incremental clustering approach. This step eliminates redundant filters, yielding a more compact optimized set of filters. The resulting filters have been evaluated using an application-oriented fitness criterion based on support vector machines. We have tested the proposed framework on real data collected in Dearborn, MI, in summer and fall 2001, using Ford's proprietary low-light camera.