Approach to Part using Deformable Part Model in Pedestrian Detection System
- Authors
- Choi, Hye Ji; Shin, Nara; Choi, 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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Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
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