Knowledge Commons of Institute of Automation,CAS
Spatio-Temporal Self-Organizing Map Deep Network for Dynamic Object Detection from Videos | |
Du, Yang1,2![]() ![]() ![]() ![]() | |
2017 | |
会议名称 | IEEE Conference on Computer Vision and Pattern Recognition (CVPR) |
会议录名称 | 2017 IEEE Conference on Computer Vision and Pattern Recognition |
会议日期 | 20170721-20170726 |
会议地点 | Honolulu, Hawaii |
摘要 | 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. |
关键词 | Dynamic Object Detection Self-organizing Map Deep Network |
学科领域 | 模式识别与智能系统 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/19728 |
专题 | 多模态人工智能系统全国重点实验室_视频内容安全 |
通讯作者 | Yuan, Chunfeng |
作者单位 | 1.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 |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 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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