Crowd counting using statistical features based on curvelet frame change detection
Automatic counting for moving crowds in digital images is an important application in computer artificial intelligence, especially for safety and management purposes. This paper presents a new method to estimate the size of a crowd. The new algorithm depends on sequential frame differences to estima...
Saved in:
Main Authors: | , |
---|---|
Format: | Article |
Published: |
Springer New York LLC
2017
|
Subjects: | |
Online Access: | http://eprints.utm.my/id/eprint/76977/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84984784814&doi=10.1007%2fs11042-016-3869-1&partnerID=40&md5=8baceb2a0458dfb7a58cb55b7f85b8aa |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.utm.76977 |
---|---|
record_format |
eprints |
spelling |
my.utm.769772018-04-30T14:30:30Z http://eprints.utm.my/id/eprint/76977/ Crowd counting using statistical features based on curvelet frame change detection Hafeezallah, A. Abu-Bakar, S. TK Electrical engineering. Electronics Nuclear engineering Automatic counting for moving crowds in digital images is an important application in computer artificial intelligence, especially for safety and management purposes. This paper presents a new method to estimate the size of a crowd. The new algorithm depends on sequential frame differences to estimate the crowd size in a scene. However, relying only on these simple differences adds more constraints for extracting sufficient crowd descriptors. A curvelet transform is employed to achieve that goal. Every two sequential frames are transformed into multi-resolution and multi-direction formats, and then the frame differences are detected at every subband in the curvelet domain. Statistical features out of each subband are then calculated, and the collected features from all subbands are considered as a descriptor vector for the crowd in the scene. Finally, a neural network is manipulated to map the descriptor vectors into predicted counts. The experimental results show that the proposed curvelet statistical features are more robust and provide crowd counting with higher accuracy than previous approaches. Springer New York LLC 2017 Article PeerReviewed Hafeezallah, A. and Abu-Bakar, S. (2017) Crowd counting using statistical features based on curvelet frame change detection. Multimedia Tools and Applications, 76 (14). pp. 15777-15799. ISSN 1380-7501 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84984784814&doi=10.1007%2fs11042-016-3869-1&partnerID=40&md5=8baceb2a0458dfb7a58cb55b7f85b8aa DOI:10.1007/s11042-016-3869-1 |
institution |
Universiti Teknologi Malaysia |
building |
UTM Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Teknologi Malaysia |
content_source |
UTM Institutional Repository |
url_provider |
http://eprints.utm.my/ |
topic |
TK Electrical engineering. Electronics Nuclear engineering |
spellingShingle |
TK Electrical engineering. Electronics Nuclear engineering Hafeezallah, A. Abu-Bakar, S. Crowd counting using statistical features based on curvelet frame change detection |
description |
Automatic counting for moving crowds in digital images is an important application in computer artificial intelligence, especially for safety and management purposes. This paper presents a new method to estimate the size of a crowd. The new algorithm depends on sequential frame differences to estimate the crowd size in a scene. However, relying only on these simple differences adds more constraints for extracting sufficient crowd descriptors. A curvelet transform is employed to achieve that goal. Every two sequential frames are transformed into multi-resolution and multi-direction formats, and then the frame differences are detected at every subband in the curvelet domain. Statistical features out of each subband are then calculated, and the collected features from all subbands are considered as a descriptor vector for the crowd in the scene. Finally, a neural network is manipulated to map the descriptor vectors into predicted counts. The experimental results show that the proposed curvelet statistical features are more robust and provide crowd counting with higher accuracy than previous approaches. |
format |
Article |
author |
Hafeezallah, A. Abu-Bakar, S. |
author_facet |
Hafeezallah, A. Abu-Bakar, S. |
author_sort |
Hafeezallah, A. |
title |
Crowd counting using statistical features based on curvelet frame change detection |
title_short |
Crowd counting using statistical features based on curvelet frame change detection |
title_full |
Crowd counting using statistical features based on curvelet frame change detection |
title_fullStr |
Crowd counting using statistical features based on curvelet frame change detection |
title_full_unstemmed |
Crowd counting using statistical features based on curvelet frame change detection |
title_sort |
crowd counting using statistical features based on curvelet frame change detection |
publisher |
Springer New York LLC |
publishDate |
2017 |
url |
http://eprints.utm.my/id/eprint/76977/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84984784814&doi=10.1007%2fs11042-016-3869-1&partnerID=40&md5=8baceb2a0458dfb7a58cb55b7f85b8aa |
_version_ |
1643657463121051648 |
score |
13.160551 |