Deep Reinforcement Learning With Visual Attention for Vehicle Classification | |
Zhao, Dongbin1,2; Chen, Yaran1; Lv, Le1 | |
发表期刊 | IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS |
2017-12-01 | |
卷号 | 9期号:4页码:356-367 |
文章类型 | Article |
摘要 | Automatic vehicle classification is crucial to intelligent transportation system, especially for vehicle-tracking by police. Due to the complex lighting and image capture conditions, image-based vehicle classification in real-world environments is still a challenging task and the performance is far from being satisfactory. However, owing to the mechanism of visual attention, the human vision system shows remarkable capability compared with the computer vision system, especially in distinguishing nuances processing. Inspired by this mechanism, we propose a convolutional neural network (CNN) model of visual attention for image classification. A visual attention-based image processing module is used to highlight one part of an image and weaken the others, generating a focused image. Then the focused image is input into the CNN to be classified. According to the classification probability distribution, we compute the information entropy to guide a reinforcement learning agent to achieve a better policy for image classification to select the key parts of an image. Systematic experiments on a surveillance-nature dataset which contains images captured by surveillance cameras in the front view, demonstrate that the proposed model is more competitive than the large-scale CNN in vehicle classification tasks. |
关键词 | Convolutional Neural Network (Cnn) Reinforcement Learning Vehicle Classification Visual Attention |
WOS标题词 | Science & Technology ; Technology ; Life Sciences & Biomedicine |
DOI | 10.1109/TCDS.2016.2614675 |
关键词[WOS] | OBJECT RECOGNITION ; FEATURES ; REPRESENTATION ; REGRESSION ; SALIENCY ; ROBOTS ; SCENES |
收录类别 | SCI ; SSCI |
语种 | 英语 |
项目资助者 | National Natural Science Foundation of China(61273136 ; National Key Research and Development Plan(2016YFB0101000) ; 61573353 ; 61533017) |
WOS研究方向 | Computer Science ; Robotics ; Neurosciences & Neurology |
WOS类目 | Computer Science, Artificial Intelligence ; Robotics ; Neurosciences |
WOS记录号 | WOS:000418069600006 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/14474 |
专题 | 复杂系统管理与控制国家重点实验室_深度强化学习 |
作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zhao, Dongbin,Chen, Yaran,Lv, Le. Deep Reinforcement Learning With Visual Attention for Vehicle Classification[J]. IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS,2017,9(4):356-367. |
APA | Zhao, Dongbin,Chen, Yaran,&Lv, Le.(2017).Deep Reinforcement Learning With Visual Attention for Vehicle Classification.IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS,9(4),356-367. |
MLA | Zhao, Dongbin,et al."Deep Reinforcement Learning With Visual Attention for Vehicle Classification".IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 9.4(2017):356-367. |
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