Combination of Feature Extraction Methods for SVM Pedestrian Detection

Parra, Ignacio; Llorca, David F.; Sotelo, Miguel Ángel; Bergasa, Luis M.; Revenga, Pedro; Nuevo, Jesús; Ocaña, Manuel; Garrido, Miguel Ángel G.
Year: 2007
Type of Publication: Article
Keywords: Detection and identification systems; Intelligent transportation systems; Machine vision; Pedestrian detectors; Pedestrians
Journal: IEEE Transactions on Intelligent Transportation Systems
Volume: 8
Number: 2
Pages: 292-307
Month: July
ISSN: 1524-9050
DOI: 10.1109/TITS.2007.894194
This paper describes a comprehensive combination of feature extraction methods for vision-based pedestrian detection in Intelligent Transportation Systems. The basic components of pedestrians are first located in the image and then combined with a support-vector-machine-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 components-based learning approach is proposed in order to better deal with pedestrian 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 at either daytime or nighttime. The results achieved to date show interesting conclusions that suggest a combination of feature extraction methods as an essential clue for enhanced detection performance
Hits: 10681