Comparison of granules features for pedestrian detection.
Journal
IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
Pages
1777-1782
Date Issued
2012
Author(s)
Abstract
Pedestrian detection is an important part of intelligent transportation systems. In the literature, Histogram of Oriented Gradients (HOG) detector for pedestrian detection is known for its good performance, but there are still some false detections appearing in the cases with flat area or clustered background. To deal with these problems, in this research work we develop a new feature which is based on pairing comparison computations, called Comparison of Granules (CoG). The idea of CoG is to encode the textural information of local area describing how different the pixel intensities are distributed within a region. It is shown that the special characteristics of CoG feature are "small" and "efficiency" relative to HOG. By incorporating this new feature, we propose a HOG-CoG detector which through our validation experiment achieves 38% log-average miss rate in full image evaluation and 90% detection rate at 10-4 false positives per window on INRIA Person Dataset. Another contribution of this work is that, we also present a training scheme that can be applied on huge database for training a detector. Such training scheme can reduce the number of hard samples during bootstrap training. © 2012 IEEE.
Event(s)
2012 15th International IEEE Conference on Intelligent Transportation Systems, ITSC 2012
SDGs
Other Subjects
Bootstrap training; Data sets; Detection rates; False detections; False positive; Flat area; Image evaluation; Intelligent transportation systems; Miss-rate; Pedestrian detection; Pixel intensities; Textural information; Training schemes; Granulation; Intelligent systems; Detectors
Type
conference paper