Combining keypoint-based and segment-based features for counting people in crowded scenes-Reference-Cited by-同舟云学术

Combining keypoint-based and segment-based features for counting people in crowded scenes

Published:2016-06 Issue: Volume:345 Page:199-216
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Hashemzadeh Mahdi,Farajzadeh Nacer

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference54 articles.

1. Video analysis using corner motion statistics;Albiol,2009

2. Speeded-up robust features (SURF);Bay;Comput. Vis. Image Underst.,2008

3. Learning OpenCV: Computer Vision with the OpenCV Library;Bradski,2008

4. Unsupervised Bayesian detection of independent motion in crowds;Brostow,2006

5. Towards a robust solution to people counting;Celik,2006

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