Pedestrian detection using structure-constrained features with ada-boost algorithm = 구조제한특징과 ada-boost 알고리즘을 이용한 보행자검출

Among various object detection tasks, pedestrian detection task is especially important since it is highly related to many industrial applications such as autonomous car and surveillance. A pedestrian detector should be able to detect the full-body rectangular regions of humans in a 2D RGB image. It is very challenging task to detect pedestrians in various poses, scales, and lightning conditions. The proposed detector uses features which are semi-automatic. Their structures are defined by finely-designed feature kernels. The shapes and sizes of the kernels are determined with some constraints which reflect various invariance characteristics. However, their positions are discriminatively learned through the Ada-boost algorithm. The proposed features are efficient to compute due to the usage of the integral image and the vectorized operations. The proposed detector shows decent performance with respect to speed and accuracy.
Advisors
Yang, Hyun Seungresearcher양현승researcher
Publisher
한국과학기술원
Issue Date
2016
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 로봇공학학제전공, 2016.2 ,[ii, 26 p. :]

Keywords

Pedestrian detection; Feature kernel; Ada-boost; Feature constraints; Object Detection; 보행자 검출; 특징커널; 특징 구속조건; 물체 검출

URI
http://hdl.handle.net/10203/221334
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=649369&flag=t
Appears in Collection
RE-Theses_Master(석사논문)
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