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
- 학술대회자료
- 저자정보
- 발행연도
- 2013.10
- 수록면
- 124 - 129 (6page)
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초록· 키워드
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This paper aims to introduce a novel robust real time system capable of rapidly detecting and tracking vehicles in a video stream using a monocular vision system. The framework used for this purpose is an actively relearned implementation of the Haar-like feature based Viola-Jones classifier capable of classifying image frame regions as a vehicle or non-vehicle. A passively trained supervised system (based on Adaboost) is initially built by cascading a set of weak classifiers working with Rectangular Haar-like features. An actively learned model is then generated from the initial passive classifier by querying misclassified instances when the model is evaluated on an independent dataset. This classifier is integrated with a Lucas-Kanade Optical Flow Tracker and an empirical distance estimation algorithm to evolve the system into a complete real-time detection and tracking system. The built model is then evaluated extensively on static as well as real world data and results are presented.
목차
Abstract
1. INTRODUCTION
2. CLASSIFIER DESIGN
3. VEHICLE TRACKING AND DISTANCE ESTIMATION
4. EVALUATION AND DISCUSSION
5. CONCLUSIONS
REFERENCES
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