A Vehicle Reidentification Algorithm Based on Double-Channel Symmetrical CNN

Lijun Yang,Tangsen Huang,Deepu Rajan

doi:10.1155/2021/8899007

Lijun Yang, Tangsen Huang + Show 1 more

Open Access

https://doi.org/10.1155/2021/8899007

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Abstract

It has become a challenging research topic to accurately identify the vehicles in the past from the mass monitoring data. The challenge is that the vehicle in the image has a large attitude, angle of view, light, and other changes, and these complex changes will seriously affect the vehicle recognition performance. In recent years, the convolutional neural network (CNN) has achieved great success in the field of vehicle reidentification. However, due to the small amount of vehicle annotation in the dataset of vehicle reidentification, the existing CNN model is not fully utilized in the training process, which affects the ability to identify the deep learning model. In order to solve the above problems, a double-channel symmetric CNN vehicle recognition algorithm is proposed by improving the network structure. In this method, two samples are taken as input at the same time, in which each sample has complementary characteristics. In this case, with limited training samples, the combination of inputs will be more diversified, and the training process of the CNN model will be more abundant. Experiments show that the recognition accuracy of the proposed algorithm is better than other existing methods, which further verifies the effectiveness of the proposed algorithm in this study.

Highlights

In recent years, the society pays more and more attention to the public security problem, and the monitoring equipment is more and more popular
The monitoring system mostly adopts the method of real-time camera and human participation to monitor. e massive monitoring data is a big problem for the personnel who are in charge of monitoring the video. ere are two reasons: (1) the monitoring system generates data in real time, resulting in a huge amount of data; (2) the real-time monitoring data records a scene with random changes, and it is difficult for the monitor staff to pay attention for a long time during the long-time observation
E proposed method in this study is compared with some existing vehicle reidentification methods, including the traditional manual design method and the deep learningbased method. e specific comparison ends are shown in Table 2. e results show that the proposed method in this study has achieved a competitive performance of vehicle reidentification, which is better than some existing vehicle reidentification methods

Summary

Introduction

The society pays more and more attention to the public security problem, and the monitoring equipment is more and more popular. Is study attempts to design a double-channel symmetrical CNN structure for vehicle reidentification by improving the network structure In this double-channel structure, two samples are input at a time. In the process of vehicle recognition, the deep learning model M obtained from network training is used as the feature extractor. E process of vehicle reidentification is to use the deep learning model obtained in the process of network training as a feature extractor. It extracts the response of the middle layer (AlexNet is the response of the FC7_concat layer and ResNet-50 is the response of the Pool5_concat layer) as the feature representation of the vehicle image in the probe set and the gallery set. On the basis of image features, the crosscamera search is performed to calculate the distance between the image features in the probe set and the gallery set, and the distance is sorted. e final vehicle recognition performance is evaluated according to the ranking list

Experimental Results and Analysis

Methods

Conclusion