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Approach to Part using Deformable Part Model in Pedestrian Detection System

Authors
Choi, Hye JiShin, NaraChoi, Kwang Nam
Issue Date
May-2016
Publisher
SPIE-INT SOC OPTICAL ENGINEERING
Keywords
Pedestrian Detection; Deformable Part Model; Histogram of Oriented Gradients; Object Detection
Citation
FIRST INTERNATIONAL WORKSHOP ON PATTERN RECOGNITION, v.0011
Journal Title
FIRST INTERNATIONAL WORKSHOP ON PATTERN RECOGNITION
Volume
0011
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/47975
DOI
10.1117/12.2242984
ISSN
0277-786X
1996-756X
Abstract
Histogram of Oriented Gradient (HOG) proposed by Dalal and Triggs is currently the most basic algorithm to detection pedestrian. The algorithm is weak to occlusion, since the algorithm trained by the image of pedestrian full body images as one feature. As a result, the detection rate using HOG feature becomes decreases remarkably. To solve this problem, the paper proposed detection system using Deformable Part-based Model (DPM) just divided two parts of pedestrian data through latent Support Vector Machine (SVM) based machine learning. Experimental results show that proposed approach achieves better performance on detection with high accuracy than existed method [1].
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Choi, Kwang Nam
소프트웨어대학 (소프트웨어학부)
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