NDLI: An efficient self-learning people counting system

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2013 2nd IAPR Asian Conference on Pattern Recognition

The First Asian Conference on Pattern Recognition

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Table of contents

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An efficient self-learning people counting system

Content Provider	IEEE Xplore Digital Library
Author	Jingwen Li Lei Huang Changping Liu
Copyright Year	2011
Description	Author affiliation: Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China (Jingwen Li; Lei Huang; Changping Liu)
Abstract	People counting is a challenging task and has attracted much attention in the area of video surveillance. In this paper, we present an efficient self-learning people counting system which can count the exact number of people in a region of interest. This system based on bag-of-features model can effectively detect the pedestrians some of which are usually treated as background because they are static or move slowly. The system can also select pedestrian and non-pedestrian samples automatically and update the classifier in real-time to make it more suitable for certain specific scene. Experimental results on a practical public dataset named CASIA Pedestrian Counting Dataset show that the proposed people counting system is robust and accurate.
Starting Page	125
Ending Page	129
File Size	1265079
Page Count	5
File Format	PDF
ISBN	9781457701221
e-ISBN	9781457701214
DOI	10.1109/ACPR.2011.6166686
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vocabulary Visualization Feature extraction Monitoring Meteorology
Content Type	Text
Resource Type	Article

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