CASIA OpenIR  > 模式识别国家重点实验室  > 视频内容安全
Spatio-Temporal Self-Organizing Map Deep Network for Dynamic Object Detection from Videos
Du, Yang1,2; Yuan, Chunfeng1; Li, Bing1; Hu, Weiming1; Maybank, Stephen3
2017
Conference NameIEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Source Publication2017 IEEE Conference on Computer Vision and Pattern Recognition
Conference Date20170721-20170726
Conference PlaceHonolulu, Hawaii
Abstract
In dynamic object detection, it is challenging to construct an effective model to sufficiently characterize the spatial-temporal properties of the background. This paper proposes a new Spatio-Temporal Self-Organizing Map (STSOM) deep network to detect dynamic objects in complex scenarios. The proposed approach has several contributions: First, a novel STSOM shared by all pixels in a video frame is presented to efficiently model complex background. We exploit the fact that the motions of complex background have the global variation in the space and the local variation in the time, to train STSOM using the whole frames and the sequence of a pixel over time to tackle the variance of complex background. Second, a Bayesian parameter estimation based method is presented to learn
thresholds automatically for all pixels to filter out the background. Last, in order to model the complex background more accurately, we extend the single-layer STSOM to the deep network. Then the background is filtered out layer by layer. Experimental results on CDnet 2014 dataset demonstrate that the proposed STSOM deep network outperforms numerous recently proposed methods in the overall performance and in most categories of scenarios.
KeywordDynamic Object Detection Self-organizing Map Deep Network
Subject Area模式识别与智能系统
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/19728
Collection模式识别国家重点实验室_视频内容安全
Corresponding AuthorYuan, Chunfeng
Affiliation1.CAS Center for Excellence in Brain Science and Intelligence Technology, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
3.Birkbeck College
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
Du, Yang,Yuan, Chunfeng,Li, Bing,et al. Spatio-Temporal Self-Organizing Map Deep Network for Dynamic Object Detection from Videos[C],2017.
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