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An adaptive block-based matching algorithm for crowd motion sequences

For crowd analytics and surveillance systems, motion estimation is an essential first step. Lots of crowd motion estimation algorithms have been presented in the last years comprising pedestrian motion. However, algorithms based on optical flow and background subtraction have numerous limitations su...

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Main Authors: Kajo, I., Kamel, N., Malik, A.S.
Format: Article
Published: Springer New York LLC 2018
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010739044&doi=10.1007%2fs11042-016-4327-9&partnerID=40&md5=066ea63593a81f4569c26f101fa9eb27
http://eprints.utp.edu.my/21257/
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spelling my.utp.eprints.212572019-02-26T03:19:03Z An adaptive block-based matching algorithm for crowd motion sequences Kajo, I. Kamel, N. Malik, A.S. For crowd analytics and surveillance systems, motion estimation is an essential first step. Lots of crowd motion estimation algorithms have been presented in the last years comprising pedestrian motion. However, algorithms based on optical flow and background subtraction have numerous limitations such as the complexity of the computation in the presence of high dense crowd and sudden motion changes. Therefore, a novel estimation algorithm is proposed to measure the motion of crowd with less computational complexity and satisfy the real time requirements. The proposed algorithm is based on block-based matching, particle advection, and social force model. By the block-based matching, the motion is estimated in each frame, and the corresponding motion field is created. The particle advection process provides more information about the behavior of pedestrians groups, their tracked trajectories and the boundary of each group segment. Relying on the social force model, a predicted direction of the motion vectors (MV) could be measured significantly. Subsequently, the block-based technique is combined with the social force model to obtain the accurate motion vector with the less possible number of search points. The experimental results indicate that the proposed method achieves high performance by reducing the search points, particularly when many collision situations or obstacles exist in the scenes. Considering the reduction in the computational complexity, the quality of degradation is very low. In all cases, average PSNR degradation of the proposed algorithm is only 0.09. © 2017, Springer Science+Business Media New York. Springer New York LLC 2018 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010739044&doi=10.1007%2fs11042-016-4327-9&partnerID=40&md5=066ea63593a81f4569c26f101fa9eb27 Kajo, I. and Kamel, N. and Malik, A.S. (2018) An adaptive block-based matching algorithm for crowd motion sequences. Multimedia Tools and Applications, 77 (2). pp. 1783-1809. http://eprints.utp.edu.my/21257/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description For crowd analytics and surveillance systems, motion estimation is an essential first step. Lots of crowd motion estimation algorithms have been presented in the last years comprising pedestrian motion. However, algorithms based on optical flow and background subtraction have numerous limitations such as the complexity of the computation in the presence of high dense crowd and sudden motion changes. Therefore, a novel estimation algorithm is proposed to measure the motion of crowd with less computational complexity and satisfy the real time requirements. The proposed algorithm is based on block-based matching, particle advection, and social force model. By the block-based matching, the motion is estimated in each frame, and the corresponding motion field is created. The particle advection process provides more information about the behavior of pedestrians groups, their tracked trajectories and the boundary of each group segment. Relying on the social force model, a predicted direction of the motion vectors (MV) could be measured significantly. Subsequently, the block-based technique is combined with the social force model to obtain the accurate motion vector with the less possible number of search points. The experimental results indicate that the proposed method achieves high performance by reducing the search points, particularly when many collision situations or obstacles exist in the scenes. Considering the reduction in the computational complexity, the quality of degradation is very low. In all cases, average PSNR degradation of the proposed algorithm is only 0.09. © 2017, Springer Science+Business Media New York.
format Article
author Kajo, I.
Kamel, N.
Malik, A.S.
spellingShingle Kajo, I.
Kamel, N.
Malik, A.S.
An adaptive block-based matching algorithm for crowd motion sequences
author_facet Kajo, I.
Kamel, N.
Malik, A.S.
author_sort Kajo, I.
title An adaptive block-based matching algorithm for crowd motion sequences
title_short An adaptive block-based matching algorithm for crowd motion sequences
title_full An adaptive block-based matching algorithm for crowd motion sequences
title_fullStr An adaptive block-based matching algorithm for crowd motion sequences
title_full_unstemmed An adaptive block-based matching algorithm for crowd motion sequences
title_sort adaptive block-based matching algorithm for crowd motion sequences
publisher Springer New York LLC
publishDate 2018
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010739044&doi=10.1007%2fs11042-016-4327-9&partnerID=40&md5=066ea63593a81f4569c26f101fa9eb27
http://eprints.utp.edu.my/21257/
_version_ 1738656263994081280
score 13.211869

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