DocumentCode :
3438992
Title :
How to Improve the Quality of Pedestrian Detection Using the Priori Knowledge
Author :
Zhiquan Qi ; Yingjie Tian ; Xiaodan Yu ; Yong Shi
Author_Institution :
Res. Center on Fictitious Econ. & Data Sci., Beijing, China
fYear :
2013
fDate :
7-10 Dec. 2013
Firstpage :
795
Lastpage :
799
Abstract :
Using the privileged information classification to solve the challenge between the speed and the quality in pedestrian detection is a new direction of research in the compute vision smca. In this paper, we apply Histogram Intersection Kernel (HIK) into Learning model Using Privileged Information (LUPI)to improve the quality of pedestrian detection problem. All experimental results show the robustness and effectiveness of the proposed method. Under the help of the privileged information and histogram intersection kernel together, the accuracy of the pedestrian detection can obtain a significant improvement.
Keywords :
computer vision; image classification; learning (artificial intelligence); object detection; pedestrians; HIK; LUPI; computer vision; histogram intersection kernel; learning model using privileged information; pedestrian detection problem quality improvement; priori knowledge; privileged information classification; Feature extraction; Histograms; Image color analysis; Kernel; Object detection; Support vector machines; Training; object detection; privileged information; support vector machine;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining Workshops (ICDMW), 2013 IEEE 13th International Conference on
Conference_Location :
Dallas, TX
Print_ISBN :
978-1-4799-3143-9
Type :
conf
DOI :
10.1109/ICDMW.2013.72
Filename :
6754002
Link To Document :
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