Modeling Temporal Interactions with Interval Temporal Bayesian Networks for Complex Activity Recognition
文献类型：期刊论文
| 作者 | Zhang, Yongmian1; Zhang, Yifan2,3 ; Swears, Eran4; Larios, Natalia4; Wang, Ziheng4; Ji, Qiang4
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| 刊名 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
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| 出版日期 | 2013-10-01 |
| 卷号 | 35期号:10页码:2468-2483 |
| 关键词 | Activity recognition temporal reasoning Bayesian networks interval temporal Bayesian networks |
| 英文摘要 | Complex activities typically consist of multiple primitive events happening in parallel or sequentially over a period of time. Understanding such activities requires recognizing not only each individual event but, more importantly, capturing their spatiotemporal dependencies over different time intervals. Most of the current graphical model-based approaches have several limitations. First, time-sliced graphical models such as hidden Markov models (HMMs) and dynamic Bayesian networks are typically based on points of time and they hence can only capture three temporal relations: precedes, follows, and equals. Second, HMMs are probabilistic finite-state machines that grow exponentially as the number of parallel events increases. Third, other approaches such as syntactic and description-based methods, while rich in modeling temporal relationships, do not have the expressive power to capture uncertainties. To address these issues, we introduce the interval temporal Bayesian network (ITBN), a novel graphical model that combines the Bayesian Network with the interval algebra to explicitly model the temporal dependencies over time intervals. Advanced machine learning methods are introduced to learn the ITBN model structure and parameters. Experimental results show that by reasoning with spatiotemporal dependencies, the proposed model leads to a significantly improved performance when modeling and recognizing complex activities involving both parallel and sequential events. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| 研究领域[WOS] | Computer Science ; Engineering |
| 关键词[WOS] | EVENT RECOGNITION ; REPRESENTATION ; TRACKING ; VIDEO ; KNOWLEDGE ; FRAMEWORK |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000323175200012 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/3354]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_图像与视频分析团队 |
| 作者单位 | 1.Konica Minolta Lab USA Inc, IT Res Div, San Mateo, CA 94403 USA 2.Rensselaer Polytech Inst, Dept Elect Comp & Syst Engn, Troy, NY 12180 USA 3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 4.Rensselaer Polytech Inst, Dept Elect Comp & Syst Engn, Troy, NY 12180 USA |
| 推荐引用方式 GB/T 7714 | Zhang, Yongmian,Zhang, Yifan,Swears, Eran,et al. Modeling Temporal Interactions with Interval Temporal Bayesian Networks for Complex Activity Recognition[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2013,35(10):2468-2483. |
| APA | Zhang, Yongmian,Zhang, Yifan,Swears, Eran,Larios, Natalia,Wang, Ziheng,&Ji, Qiang.(2013).Modeling Temporal Interactions with Interval Temporal Bayesian Networks for Complex Activity Recognition.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,35(10),2468-2483. |
| MLA | Zhang, Yongmian,et al."Modeling Temporal Interactions with Interval Temporal Bayesian Networks for Complex Activity Recognition".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 35.10(2013):2468-2483. |
入库方式： OAI收割
来源：自动化研究所
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