Attention-mechanism-based tracking method for intelligent Internet of vehicles

Mohsen Guizani,Jie Guo,Xiaojiang Du,Bin Song,Xu Kang

doi:10.1177/1550147718805946

Abstract

Vehicle tracking task plays an important role on the Internet of vehicles and intelligent transportation system. Beyond the traditional Global Positioning System sensor, the image sensor can capture different kinds of vehicles, analyze their driving situation, and can interact with them. Aiming at the problem that the traditional convolutional neural network is vulnerable to background interference, this article proposes vehicle tracking method based on human attention mechanism for self-selection of deep features with an inter-channel fully connected layer. It mainly includes the following contents: (1) a fully convolutional neural network fused attention mechanism with the selection of the deep features for convolution; (2) a separation method for template and semantic background region to separate target vehicles from the background in the initial frame adaptively; (3) a two-stage method for model training using our traffic dataset. The experimental results show that the proposed method improves the tracking accuracy without an increase in tracking time. Meanwhile, it strengthens the robustness of algorithm under the condition of the complex background region. The success rate of the proposed method in overall traffic datasets is higher than Siamese network by about 10%, and the overall precision is higher than Siamese network by 8%.

Highlights

The Internet of vehicles (IOV) can improve people’s travel efficiency through urban traffic management, traffic congestion detection, path planning, road charge, and public transportation management, to alleviate traffic congestion
Vehicle tracking is a key technology of IOV and intelligent transportation system (ITS), in which the image sensor and wireless sensor are complementary
We propose a fully convolutional neural network combined with an attention mechanism to measure the similarity of a template and a search area using selected deep features of multiple channels

Summary

Introduction

The Internet of vehicles (IOV) can improve people’s travel efficiency through urban traffic management, traffic congestion detection, path planning, road charge, and public transportation management, to alleviate traffic congestion. In literatures,[1,2,3,4] the authors try to use correlation filter of the target area on the feature map of the CNN They try the different feature layers and different structure neural networks, but the accuracy of the tracking is not improved, and the time delay of each frame is greatly increased. The target image block in the last frame and the surrounding image block are sent to two CNNs, respectively, and the output results are sent to the same full connection layer to judge the similarity degree of the two This method has omitted the online update process, and after one training, the network only forecasts each frame without two training and improves the time delay and accuracy. We conclude this article and discuss the research focus in section ‘‘Conclusion and future work.’’

Background and related work

Experiments

Conclusion and future work

Findings

Methods