Research on Vehicle Recognition Based on Unpacking 3D Bounding Boxes of Monocular Camera in Traffic Scene Changan University, China

Format:

Book

Conference/Event

Author/Creator:

Wang, Wei, author.

Contributor:

Cui, Hua

Tang, Xinyao

Tian, Shangwei

Zhang, Chaoyang

Conference Name:

3rd International Forum on Connected Automated Vehicle Highway System through the China Highway & Transportation Society (2020-10-29 : Jinan, China)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2020

Summary:

Currently, most of vehicle recognition methods are realized by deep convolutional neural networks (DCNNs) with input of images directly as training data. Due to the factor of perspective distortion and scale change of images taken by monocular camera, a large number of multi-scale images need to be used for training, and physical information of vehicles cannot be obtained at the same time. In order to improve the above problems, we present a method of vehicle recognition based on unpacking 3D bounding boxes in this paper. Firstly, camera calibration information and geometric constraints are used to build 3D bounding boxes around vehicles in monocular projection. Then, the 3D bounding boxes are unpacked to obtain 3D normalized spatial data without perspective distortion. Finally, VGG-16 is chosen as the backbone of our network, the output of which can be divided into five common vehicle types including hatchback, sedan, SUV, truck and bus. The experimental results indicate that the accuracy of our method is improved by 8.74% for hatchback and 7.49% for sedan with less training data, which outperforms traditional end-to-end deep learning methods of vehicle recognition and physical information can be obtained simultaneously

Notes:

Vendor supplied data

Publisher Number:

2020-01-5196

Access Restriction:

Restricted for use by site license