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請使用永久網址來引用或連結此文件:
http://ir.ncue.edu.tw/ir/handle/987654321/11742
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| 題名: | Understanding of Human Behaviors from Videos in Nursing Care Monitoring System |
| 作者: | Liu, Chin-De;Chung, Pau-Choo;Chung, Yi-Nung;Thonnat, Monique |
| 貢獻者: | 電機工程學系 |
| 關鍵詞: | Behavior analysis;Activity recognition;State machine;Scenario recognition |
| 日期: | 2007-02
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| 上傳時間: | 2012-07-02T02:06:36Z
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| 出版者: | IOS Press |
| 摘要: | This paper addresses the issue in scenario-based understanding of human behavior from videos in a nursing care monitoring system. The analysis is carried out based on experiments consisting of single-state scenarios and multi-state scenarios where the former monitors activities under contextual contexts for elementary behavior reasoning, while the latter dictating the elementary behavior order for behavior reasoning, with a priori knowledge in system profile for normality detection. By integrating the activities, situation context, and profile knowledge we can have a better understanding of patients in a monitoring system. In activity recognition, a Negation-Selection mechanism is developed. which employs a divide-and-conquer concept with the Negation using posture transition to preclude the negative set from the activities. The Selection that follows the Negation uses a moving history trace for activity recognition. Such a history trace composes not only the pose from single frame, but also history trajectory information. As a result, the activity can be more accurately identified. The developed approach has been established into a nursing care monitoring system for elder's daily life behaviors. Results have shown the promise of the approach which can accurately interpret 85% of the regular daily behavior. In addition, the approach is also applied to accident detection which was found to have 90% accuracy with 0% false alarm. |
| 關聯: | Journal of High Speed Network, 16(1): 91-103 |
| 顯示於類別: | [電機工程學系] 期刊論文
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