CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Multiple deep features learning for object retrieval in surveillance videos
Guo, Haiyun; Wang, Jinqiao; Lu, Hanqing
Source PublicationIET COMPUTER VISION
2016-02-26
Volume10Issue:4Pages:268-271
SubtypeArticle
AbstractEfficient indexing and retrieving objects of interest from large-scale surveillance videos are a significant and challenging topic. In this study, the authors present an effective multiple deep features learning approach for object retrieval in surveillance videos. Based on the discriminative convolutional neural network (CNN), they can learn multiple deep features to comprehensively describe the visual object. To be specific, they utilise the CNN model pre-trained on ImageNet ILSVRC12 and fine-tuned on our dataset to abstract structure information. In addition, they train another CNN model supervised by 11 colour names to deliver the colour information. To improve the retrieval performance, the deep features are encoded into short binary codes by locality-sensitive hash and fused to fast retrieve the object of interest. Retrieval experiments are performed on a dataset of 100k objects extracted from multi-camera surveillance videos. Comparison results with other common visual features show the effectiveness of the proposed approach.
KeywordObject Retrieval Multiple Deep Features Learning Convolutional Neural Network
WOS HeadingsScience & Technology ; Technology
DOI10.1049/iet-cvi.2015.0291
Indexed BySCI
Language英语
Funding Organization863 Program(2014AA015104) ; National Natural Science Foundation of China(61273034 ; 61332016)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000380260100005
Citation statistics
Cited Times:8[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12168
Collection模式识别国家重点实验室_图像与视频分析
Corresponding AuthorWang, Jinqiao
AffiliationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, 95 Zhongguancun East Rd, Beijing, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Guo, Haiyun,Wang, Jinqiao,Lu, Hanqing. Multiple deep features learning for object retrieval in surveillance videos[J]. IET COMPUTER VISION,2016,10(4):268-271.
APA Guo, Haiyun,Wang, Jinqiao,&Lu, Hanqing.(2016).Multiple deep features learning for object retrieval in surveillance videos.IET COMPUTER VISION,10(4),268-271.
MLA Guo, Haiyun,et al."Multiple deep features learning for object retrieval in surveillance videos".IET COMPUTER VISION 10.4(2016):268-271.
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