Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Machine Learning
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
Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
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
An Experimental Study on Pedestrian Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic algorithms in classifier fusion
Applied Soft Computing
Stereo- and neural network-based pedestrian detection
IEEE Transactions on Intelligent Transportation Systems
An adaptable time-delay neural-network algorithm for image sequence analysis
IEEE Transactions on Neural Networks
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Automated pedestrian detection is a forward looking challenge for future driver support systems in automotive industry. Such system would have to make safety critical decisions based on poor quality images shot in real-time from the unstable moving vehicles. The proposed system offers a simple yet very effective detection methodology based on mixture of Gaussians (MoG) aided by an Expectation-Maximisation (EM) clustering algorithm. The algorithm operates on a number of features built by aggregation of different variations of the first and second order pixel gradients related to the aggregated templates of pedestrian and non-pedestrian classes. For each class the algorithm fits a fixed number of clusters and using Gaussian kernels optimises the parameters of the Gaussian Mixture model such that the probabilities of belonging to the intraclass clusters is maximised. Given a new image the system instantly generates relative features and uses mixture model to build posterior probability densities for all clusters and after aggregation and renormalisation, posterior class probabilities. The system has been fine-tuned against its parameters and feature subsets and tested using almost 10000 real images provided by DaimlerChrysler. Reaching the testing performance in excess of 95% the model was announced the winner of the NISIS Competition 2007.