Pedestrian Detection Using SVM and Multi-Feature Combination

Sotelo, Miguel Ángel; Parra, Ignacio; Llorca, David F.; Naranjo, J. Eugenio
Year: 2006
Type of Publication: In Proceedings
Keywords: SVM; by-components learning approach; cluttered road images; feature extraction methods; image understanding; intelligent transportation systems; multifeature combination; occlusions; stereo vision; subtractive clustering attention mechanism; traffic images; vision-
Book title: Intelligent Transportation Systems Conference, 2006. ITSC '06. IEEE
Pages: 103 -108
Month: sept.
DOI: 10.1109/ITSC.2006.1706726
This paper describes a comprehensive combination of feature extraction methods for vision-based pedestrian detection in the framework of intelligent transportation systems. The basic components of pedestrians are first located in the image and then combined with a SVM-based classifier. This poses the problem of pedestrian detection in real, cluttered road images. Candidate pedestrians are located using a subtractive clustering attention mechanism based on stereo vision. A by-components learning approach is proposed in order to better deal with pedestrians variability, illumination conditions, partial occlusions, and rotations. Extensive comparisons have been carried out using different feature extraction methods, as a key to image understanding in real traffic conditions. A database containing thousands of pedestrian samples extracted from real traffic images has been created for learning purposes, either at daytime and nighttime. The results achieved up to date show interesting conclusions that suggest a combination of feature extraction methods as an essential clue for enhanced detection performance
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