Refine pedestrian detections by referring to features in different ways = 다른 방식으로의 특징 참조를 통한 보행자 검출 개선

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Region Proposal Network (RPN) itself which is used for region proposals in Faster R-CNN can be used as a pedestrian detector. Also, RPN even shows better performance than Faster R-CNN for pedestrian detection. However, RPN generates severe false positives such as high score backgrounds and double detections because it does not have downstream classifier. From this observations, we made a network to refine results generated from the RPN. Our Refinement Network refers to the feature maps of the RPN and trains the network to rescore severe false positives. Also, we found that different type of feature referencing method is crucial for improving performance.
Advisors
Kim, Junmoresearcher김준모researcher
Description
한국과학기술원 :전기및전자공학부,
Publisher
한국과학기술원
Issue Date
2018
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2018.2,[v, 30 p. :]

Keywords

Object detection▼apedestrian detection▼adeep learning▼amachine learning▼afeature extraction; 물체 검출▼a보행자 검출▼a딥러닝▼a기계학습▼a특징 추출

URI
http://hdl.handle.net/10203/266745
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=733960&flag=dissertation
Appears in Collection
EE-Theses_Master(석사논문)
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