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题名:
Accurate object recognition with assembling appearance and motion information
作者: Chang, Yongxin1,2,3; Yu, Huapeng1,2,3; Xu, Zhiyong1; Zhang, Jing2; Gao, Chunming2
刊名: Mathematical Problems in Engineering
出版日期: 2014
卷号: 2014, 页码:195941
学科分类: Semantics
DOI: 10.1155/2014/195941
通讯作者: Chang, Yongxin
文章类型: 期刊论文
中文摘要: How to effectively detect object and accurately give out its visible parts is a major challenge for object detection. In this paper we propose an explicit occlusion model through integrating appearance and motion information. The model combines together two parts: part-level object detection with single frame and object occlusion estimation with continuous frames. It breaks through the performance bottleneck caused by lack of information and effectively improves object detection rate under severe occlusion. Through reevaluating the semantic parts, the detecting performance of partial object detectors is largely enhanced. The explicit model enables the partial detectors to have the capability of occlusion estimation. By discarding the geometric representation in rigid single-angle perspective and applying effective pattern of objective shape, our proposed approaches greatly improve the performance and robustness of similarity measurement. For validating the performance of proposed methods, we designed a comparative experiment on challenging pedestrian frame sequences database. The experimental results on challenging pedestrian frame sequence demonstrate that, compared to the traditional algorithms, the methods proposed in this paper have significantly improved the detection rate for severe occlusion. Furthermore, it also can achieve better localization of semantic parts and estimation of occluding.
英文摘要: How to effectively detect object and accurately give out its visible parts is a major challenge for object detection. In this paper we propose an explicit occlusion model through integrating appearance and motion information. The model combines together two parts: part-level object detection with single frame and object occlusion estimation with continuous frames. It breaks through the performance bottleneck caused by lack of information and effectively improves object detection rate under severe occlusion. Through reevaluating the semantic parts, the detecting performance of partial object detectors is largely enhanced. The explicit model enables the partial detectors to have the capability of occlusion estimation. By discarding the geometric representation in rigid single-angle perspective and applying effective pattern of objective shape, our proposed approaches greatly improve the performance and robustness of similarity measurement. For validating the performance of proposed methods, we designed a comparative experiment on challenging pedestrian frame sequences database. The experimental results on challenging pedestrian frame sequence demonstrate that, compared to the traditional algorithms, the methods proposed in this paper have significantly improved the detection rate for severe occlusion. Furthermore, it also can achieve better localization of semantic parts and estimation of occluding.
收录类别: SCI ; Ei
项目资助者: National Science Foundation of China (NSF) [61205004] ; Graduate Innovation Fund of Chinese Academy of Sciences [A08K001]
语种: 英语
WOS记录号: WOS:000345040300001
ISSN号: 1024123X
Citation statistics:
内容类型: 期刊论文
URI标识: http://ir.ioe.ac.cn/handle/181551/5074
Appears in Collections:光电探测与信号处理研究室(五室)_期刊论文

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作者单位: 1. Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, China
2. School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu, China
3. Graduate University, Chinese Academy of Sciences, Beijing, China

Recommended Citation:
Chang, Yongxin,Yu, Huapeng,Xu, Zhiyong,et al. Accurate object recognition with assembling appearance and motion information[J]. Mathematical Problems in Engineering,2014,2014:195941.
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