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논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2007.8
- 수록면
- 461 - 464 (4page)
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초록· 키워드
오류제보하기
Human detection techniques in outdoor scenes have been studied for a long time to watch suspicious movements or to keep someone from danger. However there are few methods of human detection in overhead or near-field view scenes, while lots of human detection methods in far-field view scenes have been developed. In this paper, a set of four features useful for human detection in overhead view scenes and another one in near-field view scenes are suggested. Eight feature-candidates are first extracted by analyzing geometrically varying characteristics of moving objects in samples of video sequences. Then four most useful features for each view scene in classifying human from other moving objects are selected among them by using a neural network learning technique. Through experiments with hundreds of video sequences, we found that each set of features was very useful for human detection and classification accuracy for overhead view and near-field view scenes was about 99%. The suggested sets of features can be used effectively in a PTZ camera based surveillance system where both the overhead and near-field view scenes appear.
목차
ABSTRACT
1. INTRODUCTION
2. CLASSIFICATION OF CAMERA VIEWS
3. FEATURE0CANDIDATE EXTRACTION
4. SELECTION AND COMPARISION OF USEFUL FEATURE FOR CLASSIFICATION
5. EXPERIMENTAL RESULTS AND DISCUSSIONS
6. CONCLUSIONS
ACKNOWLEDGEMENT
7. REFERENCES
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UCI(KEPA) : I410-ECN-0101-2012-004-004266056