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A trajectory clustering approach to crowd flow segmentation in videos
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Sharma, Rahul and Guha, Tanaya (2016) A trajectory clustering approach to crowd flow segmentation in videos. In: 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA, 25-28 Sep 2016 pp. 1200-1204. ISBN 9781467399616. doi:10.1109/ICIP.2016.7532548 ISSN 2381-8549.
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WRAP-trajectory-clustering-approach-crowd-flow-segmentation-videos-Guha-2018.pdf - Accepted Version - Requires a PDF viewer. Download (6Mb) | Preview |
Official URL: http://dx.doi.org/10.1109/ICIP.2016.7532548
Abstract
This work proposes a trajectory clustering-based approach for segmenting flow patterns in high density crowd videos. The goal is to produce a pixel-wise segmentation of a video sequence (static camera), where each segment corresponds to a different motion pattern. Unlike previous studies that use only motion vectors, we extract full trajectories so as to capture the complete temporal evolution of each region (block) in a video sequence. The extracted trajectories are dense, complex and often overlapping. A novel clustering algorithm is developed to group these trajectories that takes into account the information about the trajectories’ shape, location, and the density of trajectory patterns in a spatial neighborhood. Once the trajectories are clustered, final motion segments are obtained by grouping of the resulting trajectory clusters on the basis of their area of overlap, and average flow direction. The proposed method is validated on a set of crowd videos that are commonly used in this field. On comparison with several state-of-the-art techniques, our method achieves better overall accuracy.
| Item Type: | Conference Item (Paper) | ||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TR Photography |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
| Library of Congress Subject Headings (LCSH): | Digital video, Algorithms, Crowds | ||||||
| Publisher: | IEEE | ||||||
| ISBN: | 9781467399616 | ||||||
| ISSN: | 2381-8549 | ||||||
| Book Title: | 2016 IEEE International Conference on Image Processing (ICIP) | ||||||
| Official Date: | 2016 | ||||||
| Dates: |
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| Page Range: | pp. 1200-1204 | ||||||
| DOI: | 10.1109/ICIP.2016.7532548 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Reuse Statement (publisher, data, author rights): | © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
| Date of first compliant deposit: | 30 October 2018 | ||||||
| Date of first compliant Open Access: | 31 October 2018 | ||||||
| Conference Paper Type: | Paper | ||||||
| Title of Event: | 2016 IEEE International Conference on Image Processing (ICIP) | ||||||
| Type of Event: | Conference | ||||||
| Location of Event: | Phoenix, AZ, USA | ||||||
| Date(s) of Event: | 25-28 Sep 2016 |
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