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
- 학위논문
- 저자정보
- 지도교수
- 김대진
- 발행연도
- 2014
- 저작권
- 포항공과대학교 논문은 저작권에 의해 보호받습니다.
이용수2
이 논문의 연구 히스토리 (4)
- 이 논문의 후속연구가 궁금하신가요?
- 연관 학술논문 또는 학술발표를 통해 보다 발전된 연구결과를 확인하실 수 있습니다.
- 이 논문의 연구 히스토리 확인하기
초록· 키워드
오류제보하기
The ability of most existing approaches to classify abandoned and removed objects (ARO) in images is affected by external environmental conditions such as illumination and traffic volume because the approaches use several pre-defined threshold values and generate many falsely-classified static regions. To reduce these effects, we propose an accurate ARO classification method using a hierarchical finite state machine (FSM) that consists of pixel-layer, region-layer, and event-layer FSM, where the result of the lower-layer FSM is used as the input of the higher-layer FSM. Each FSM is defined by a Mealy state machine with three states and several state transitions, where a support vector machine (SVM) determines the state transition based on the current state and input features such as area, intensity, motion, shape, time duration, color and edge. Because it uses the hierarchical FSM (H-FSM) structure with features that are optimally trained by SVM classifiers, the proposed ARO classification method does not require threshold values and guarantees better classification accuracy under severe environmental changes. In experiments, the proposed ARO classification method provided much higher classification accuracy and lower false alarm rate than the state-of-the-art methods in both of a public database and a commercial database. The proposed ARO classification method can be applied to many practical applications such as detection of littering, illegal parking, theft, and camouflaged soldier.
목차
1 Introduction1.1 Thesis Goal1.2 Previous Work1.3 Challenges and Difficulties1.4 Main Contributions of Thesis Work2 Theoretical Backgrounds2.1 Finite State Machine2.2 Support Vector Machine3 Abandoned and Removed Object Classification Using Hierarchical Finite State Machine3.1 The Proposed ARO Classification Method3.1.1 Pixel-layer Processing3.1.2 Region-layer Processing3.1.3 Event-layer Processing3.2 Algorithm Procedure4 Experimental Results and Discussion4.1 Experimental Setup4.2 Experiment I: Pixel-layer Classification4.3 Experiment II: Region-layer Classification4.3.1 Public Database4.3.2 Commercial Database4.3.3 Case Studies4.4 Experiment III: Event-layer Classification4.5 Experiment IV: Computational Time Analysis4.6 Real Applications5 ConclusionReferences
최근 본 자료
댓글(0)
0