Single Shot MultiBox Detector for Vehicles and Pedestrians Detection and Classification
In recent years, with the rapid development of national economy and the acceleration of urbanization, the road traffic environment has become more and more complex, and the safety and security of people's daily travel are paid more and more attention. It’s worthy to detect vehicles and pedestrians by using deep learning algorithm. In this paper, a method using the Single Shot MultiBox Detector to detect vehicles and pedestrians is introduced and compared with the others. Furthermore, the development of vehicle and pedestrian detection in the field of deep learning is discussed. INTRODUCTION Object detection is one of the main topics in the field of computer vision. 1 The core task of object detection is to locate the objects in the images, distinguish the object category and give the bounding box of each object. However, due to the distortion caused by the factors such as perspective, occlusion and posture, object detection has become a certain challenge. The traditional method for object detection can be mainly divided into six steps: denoising, window sliding, feature extraction, feature selection, feature classification, calculating the bounding box, of witch, feature extraction and feature classification are the key points. Because the traffic environment is so complex and changeable that even if the best nonlinear classifiers are used for feature classification, the accuracy of object detection and anti-interference ability will fail to meet the actual requirement. In order to extract better features from the object, in 2006, Hinton put forward the conception of deep learning, which is implemented by using the deep neural networks (DNN) to automatically learn high-level features from a large amount of sample data. 2 With the continuous development of deep learning, several deep learning networks with different functions have been proposed, such as Multi-layer Perceptron (MLP) 3 , Convolutional Neural Network (CNN) 4 , Recurrent Neural Network (RNN) 5 and Generative Adversarial Network (GAN) 6 . Researchers found that by using convolutional neural network to detect the object, the accuracy can be greatly improved and anti-interference ability will be much better. 4 This is because the convolutional neural Qiong WU 1 Sheng-bin LIAO 2 First author, Qiong WU, National Engineering Research Center For E-Learning of Central China Normal University, Lane 152, Luoyu Road, Hongshan District, Wuhan, China. Second author, Sheng-bin LIAO, National Engineering Research Center For E-Learning of Central China Normal University, Lane 152, Luoyu Road, Hongshan District, Wuhan, China. Corresponding author: wuqiong@mails.ccnu.edu.cn