A concurrent neural network approach to pedestrian detection in thermal imagery

The paper presents an original approach for pedestrian detection in thermal imagery using the neural network classifier called Concurrent Self-Organizing Maps (CSOM), previously introduced by first author; it represents a winner-takes-all collection of neural modules. The considered algorithm has th...

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Bibliographic Details
Published in2012 9th International Conference on Communications (COMM) pp. 133 - 136
Main Authors Neagoe, Victor-Emil, Ciotec, A., Barar, A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2012
Subjects
Classification algorithms
concurrent self-organizing maps (CSOM)
Feature extraction
histogram of oriented gradients (HOG)
Histograms
night vision
pedestrian detection
Principal component analysis
Self organizing feature maps
thermal imagery
Training
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Summary:The paper presents an original approach for pedestrian detection in thermal imagery using the neural network classifier called Concurrent Self-Organizing Maps (CSOM), previously introduced by first author; it represents a winner-takes-all collection of neural modules. The considered algorithm has the following main stages: (a) detection of the regions of interest (ROI); (b) feature selection using the Histogram of Oriented Gradients (HOG); (c) classification using a CSOM classifier with several neural modules for each class; (d) decision fusion of the SOM modules into the two final classes: pedestrians and non-pedestrians. For training and testing the proposed algorithm, we have used the OTCBVS-OSU Thermal Pedestrian Database provided by the Ohio State University. One obtains the False Positive Error Rate (FPER) of 1.79%, the False Negative Error Rate (FNER) of 0.49% and the Total Success Rate (TSR) of 98.48 %.
ISBN:1457700573
9781457700576
DOI:10.1109/ICComm.2012.6262539