Extensive exploration of comprehensive vehicle attributes using D-CNN with weighted multi-attribute strategy
文献类型:期刊论文
作者 | Yan, Zhuo1,2; Feng, Youji1; Cheng, Cheng1; Fu, Jianting1,2; Zhou, Xiangdong1,3; Yuan, Jiahu1 |
刊名 | IET INTELLIGENT TRANSPORT SYSTEMS |
出版日期 | 2018-04-01 |
卷号 | 12期号:3页码:186-193 |
ISSN号 | 1751-956X |
关键词 | Object Recognition Feedforward Neural Nets Learning (Artificial Intelligence) Comprehensive Vehicle Attributes D-cnn Weighted Multiattribute Strategy Deep Convolutional Neural Network Surveillance Images Vehicle Model Recognition Make Recognition Mtl Methods Multitask Learning Compcars Vehicle Dataset Manufacturer Recognition |
DOI | 10.1049/iet-its.2017.0066 |
英文摘要 | As a classical machine learning method, multi-task learning (MTL) has been widely applied in computer vision technology. Due to deep convolutional neural network (D-CNN) having strong ability of feature representation, the combination of MTL and D-CNN has attracted much attention from researchers recently. However, this kind of combination has rarely been explored in the field of vehicle analysis. The authors propose a D-CNN enhanced with weighted multi-attribute strategy for extensive exploration of comprehensive vehicle attributes over surveillance images. Specifically, regarding to recognising vehicle model and make/manufacturer, several related attributes as auxiliary tasks are incorporated in the training process of D-CNN structure. The proposed strategy focuses more on the main task compared with traditional MTL methods, which has assigned different weights for the main task and auxiliary tasks rather than treating all involved tasks equally. To the extent of their knowledge, this is the first report relating to the combination of D-CNN and weighted MTL for exploration of comprehensive vehicle attributes. The following experiments will show that the proposed approach outperforms the state-of-the-art method for the vehicle recognition and improves the accuracy rate by about 10% for the analysis of other vehicle attributes on the recently public CompCars dataset. |
资助项目 | National Natural Science Foundation of China[61502444] ; Chinese Academy of Sciences[XDA06040103] ; Chongqing Municipal Science and Technology Commission[cstc2014jcyjA10036] |
WOS研究方向 | Transportation |
语种 | 英语 |
出版者 | INST ENGINEERING TECHNOLOGY-IET |
WOS记录号 | WOS:000426545900003 |
源URL | [http://119.78.100.138/handle/2HOD01W0/6256] |
专题 | 智能安全技术研究中心 |
作者单位 | 1.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Automated Reasoning & Cognit Key Lab Chongqing, Chongqing 400714, Peoples R China |
推荐引用方式 GB/T 7714 | Yan, Zhuo,Feng, Youji,Cheng, Cheng,et al. Extensive exploration of comprehensive vehicle attributes using D-CNN with weighted multi-attribute strategy[J]. IET INTELLIGENT TRANSPORT SYSTEMS,2018,12(3):186-193. |
APA | Yan, Zhuo,Feng, Youji,Cheng, Cheng,Fu, Jianting,Zhou, Xiangdong,&Yuan, Jiahu.(2018).Extensive exploration of comprehensive vehicle attributes using D-CNN with weighted multi-attribute strategy.IET INTELLIGENT TRANSPORT SYSTEMS,12(3),186-193. |
MLA | Yan, Zhuo,et al."Extensive exploration of comprehensive vehicle attributes using D-CNN with weighted multi-attribute strategy".IET INTELLIGENT TRANSPORT SYSTEMS 12.3(2018):186-193. |
入库方式: OAI收割
来源:重庆绿色智能技术研究院
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