Analysis of Feature Fusion Based on HIK SVM and Its Application for Pedestrian Detection

This work presents the fusion of integral channel features to improve the effectiveness and efficiency of pedestrian detection. The proposed method combines the histogram of oriented gradient (HOG) and local binary pattern (LBP) features by a concatenated fusion method. Although neural network (NN)...

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Bibliographic Details
Published in:Abstract and Applied Analysis 2013-01, Vol.2013 (2013), p.182-192-521
Main Authors: Su, Song-Zhi, Chen, Shu-Yuan
Format: Article
Language:English
Subjects:
Channels
Classification
Engineering research
Histograms
Image detection
Machine learning
Management
Mathematical research
Methods
Neural networks
Pedestrian areas
Pedestrians
Safety and security measures
Science
Studies
Support vector machines
Traffic safety
Training
Visual
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Summary:This work presents the fusion of integral channel features to improve the effectiveness and efficiency of pedestrian detection. The proposed method combines the histogram of oriented gradient (HOG) and local binary pattern (LBP) features by a concatenated fusion method. Although neural network (NN) is an efficient tool for classification, the time complexity is heavy. Hence, we choose support vector machine (SVM) with the histogram intersection kernel (HIK) as a classifier. On the other hand, although many datasets have been collected for pedestrian detection, few are designed to detect pedestrians in low-resolution visual images and at night time. This work collects two new pedestrian datasets—one for low-resolution visual images and one for near-infrared images—to evaluate detection performance on various image types and at different times. The proposed fusion method uses only images from the INRIA dataset for training but works on the two newly collected datasets, thereby avoiding the training overhead for cross-datasets. The experimental results verify that the proposed method has high detection accuracies even in the variations of image types and time slots.
ISSN:1085-3375
1687-0409
DOI:10.1155/2013/436062