Abstract

Feature-based pedestrian detection method is currently the mainstream direction to solve the problem of pedestrian detection. In this kind of method, whether the appropriate feature can be extracted is the key to the comprehensive performance of the whole pedestrian detection system. It is believed that the appearance of a pedestrian can be better captured by the combination of edge/local shape feature and texture feature. In this field, the current method is to simply concatenate HOG (histogram of oriented gradient) features and LBP (local binary pattern) features extracted from an image to produce a new feature with large dimension. This kind of method achieves better performance at the cost of increasing the number of features. In this paper, Choquet integral based on the signed fuzzy measure is introduced to fuse HOG and LBP descriptors in parallel that is expected to improve accuracy without increasing feature dimensions. The parameters needed in the whole fusion process are optimized by a training algorithm based on genetic algorithm. This architecture has three advantages. Firstly, because the fusion of HOG and LBP features is parallel, the dimensions of the new features are not increased. Secondly, the speed of feature fusion is fast, thus reducing the time of pedestrian detection. Thirdly, the new features after fusion have the advantages of HOG and LBP features, which is helpful to improve the detection accuracy. The series of experimentation with the architecture proposed in this paper reaches promising and satisfactory results.

Highlights

  • Pedestrian detection is the key technology of intelligent transportation [1,2,3]

  • We extract positive samples from INRIA training set according to the pedestrian coordinates marked in the dataset, and construct negative samples from the training set by randomly cropping

  • In order to validate the performance of parallel-HOG-histogram of local binary patterns (HOLBP) features and relevant GA-based pedestrian detection algorithm proposed in this paper, four classifiers with different combinations of features are selected to be tested on the same set of testing set

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Summary

Introduction

Pedestrian detection is the key technology of intelligent transportation [1,2,3]. In addition, the core technologies included in pedestrian detection are indispensable for other applications, such as, robotics, video surveillance and behavior prediction [4,5,6]. Choquet integral based on the fuzzy measure is applied to realize the parallel fusion of HOG and LBP feature descriptors This methodology is expected to improve the detection accuracy without increasing the feature dimension. HOG features and histogram of LBP descriptors of each cell are extracted from original image, respectively They are parallelly fused by Choquet integral with its internal parameters, i.e., values of fuzzy measure, being optimized by a genetic algorithm. This fusion results in a new set of features, called parallel-HOGHOLBP (histogram of gradient—histogram of local binary patterns), which is transmitted to SVM for classification.

Histogram of Oriented Gradient Feature Extraction
Histogram of LBP Descriptor
Choquet Integral as Aggregation Tool
Feature Fusion by Choquet Integral
Pedestrian Detection Framework with Parameters Retrieved by Genetic Algorithm
Parameters Retrieving under Genetic Algorithm Framework
Classifier Training
Classifier Training and Evaluation Criterion
Experimental Results and Analysis
Conclusions
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