지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전기학회 Journal of Electrical Engineering & Technology Journal of Electrical Engineering & Technology Vol.7 No.1
- 발행연도
- 2012.1
- 수록면
- 115 - 123 (9page)
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초록· 키워드
오류제보하기
A video analysis system used to detect events in video streams generally has several processes, including object detection, object trajectories analysis, and recognition of the trajectories by comparison with an a priori trained model. However, these processes do not work well in a complex environment that has many occlusions, mirror effects, and/or shadow effects. We propose a new approach to a context-aware video surveillance system to detect predefined contexts in video streams. The proposed system consists of two modules: a feature extractor and a context recognizer. The feature extractor calculates the moving energy that represents the amount of moving objects in a video stream and the stationary energy that represents the amount of still objects in a video stream. We represent situations and events as motion changes and stationary energy in video streams. The context recognizer determines whether predefined contexts are included in video streams using the extracted moving and stationary energies from a feature extractor. To train each context model and recognize predefined contexts in video streams, we propose and use a new ensemble classifier based on the AdaBoost algorithm, DAdaBoost, which is one of the most famous ensemble classifier algorithms. Our proposed approach is expected to be a robust method in more complex environments that have a mirror effect and/or a shadow effect.
목차
Abstract
1. Introduction
2. Context-Aware System
3. Moving and Stationary Energy
4. Diverse AdaBoost Model
5. Experiments
6. Conclusion
Acknowledgements
References
참고문헌 (20)
참고문헌 신청
1. [학술지(정기간행물)] - H.H. Nagel / 1988 / From Image Sequences Towards Conceptual Descriptions / Image and Vision Computing 6 (2) : 59 ~ 74
2. [학술대회논문] - Thomas M. Strat / 1993 / Employing Contextual Information in Computer Vision / Proceedings of ARPA Image Understanding Workshop : 217 ~ 229
3. [학술지(정기간행물)] - Gerard Medioni / 2001 / Event Detection and Analysis from Video Streams / IEEE Trans. Pattern Analysis and Machine Intelligence 23 (8) : 873 ~ 889
4. [학술지(정기간행물)] - Gary R. Bradski / 2002 / Motion segmentation and pose recognition with motion history gradients / Int. Journal Machine Vision and Applications 13 (3) : 174 ~ 184
5. [학술대회논문] - Aaron Bobick / 1996 / an appearancebased representation of action / Proceedings of international conference on Pattern Recognition 1 : 307 ~ 312
2. [학술대회논문] - Thomas M. Strat / 1993 / Employing Contextual Information in Computer Vision / Proceedings of ARPA Image Understanding Workshop : 217 ~ 229
3. [학술지(정기간행물)] - Gerard Medioni / 2001 / Event Detection and Analysis from Video Streams / IEEE Trans. Pattern Analysis and Machine Intelligence 23 (8) : 873 ~ 889
4. [학술지(정기간행물)] - Gary R. Bradski / 2002 / Motion segmentation and pose recognition with motion history gradients / Int. Journal Machine Vision and Applications 13 (3) : 174 ~ 184
5. [학술대회논문] - Aaron Bobick / 1996 / an appearancebased representation of action / Proceedings of international conference on Pattern Recognition 1 : 307 ~ 312
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UCI(KEPA) : I410-ECN-0101-2013-560-001397462