A novel pedestrian detection algorithm based on data fusion of face images

Jianhu Zheng,Jinshuan Peng

doi:10.1177/1550147719845276

Abstract

In order to facilitate effective crime prevention and to issue timely warnings for the sake of public security, it is important to pinpoint the accurate position of particular pedestrians in crowded areas. Face recognition is the most popular method to detect and track pedestrian movement. During the face recognition process, feature classification ability and reliability are determined by the feature extraction methods. The primary challenge for researchers is to obtain a stable result while the targeted face is subject to varying conditions—particularly of illumination. To address this issue, we propose a novel pedestrian detection algorithm with multisource face images, which involves a face recognition algorithm based on the conjugate orthonormalized partial least-squares regression analysis under a complex lighting environment. Statistical learning theory is a research specialization of machine learning, especially applicable to small samples. Building upon the theoretical principles used to solve small-sample statistical problems, a new hypothesis has been developed; using this concept, we integrate the conjugate orthonormalized partial least-squares regression with the revised support vector machine algorithm to undertake the solution of the facial recognition problem. The experimental result proves that our algorithm achieves better performance when compared with other state-of-the-art methodologies, both numerically and visually.

Highlights

Owing to the rapid development of the economy, an increasing number of people like to participate in various activities in public areas
Existing pedestrian detection can be categorized into two groups: one group is characterized by a manual detection of pedestrians through visual inspection techniques and algorithms such as local binary patterns (LBP)[2] and histogram of oriented gradients (HOG);[3] another group is characterized by data-driven features which use learning models based on data to distinguish pedestrian features such as convolutional neural network (CNN)[4] and deep belief network (DBN).[5]
We propose a novel face recognition algorithm based on the conjugate orthonormalized partial least-squares regression analysis, which is suitable for the detection of pedestrians under complex lighting environments

Summary

Introduction

Owing to the rapid development of the economy, an increasing number of people like to participate in various activities in public areas. The subspace method based on linear projection is the most influential step in the feature extraction process of facial recognition It extracts original sample distribution information or a lowdimensional characteristic of basic classified information contained within an image matrix or vector, through the corresponding algebraic method, and completes the process of face recognition. In automatic target recognition system design, the elimination or suppression of the effect of light is a challenging task In this situation, the traditional algorithms deal with the challenge through the following approaches: (1) A single face sample image is derived using the gray histogram equalization algorithm for the object.

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Conclusion