A new clustering approach for group detection in scene-independent dense crowds
Despite significant progress in crowd behaviour analysis over the past few years, most of today's state of the art algorithms focus on analysing individual behaviour in a specific-scene. Recently, the widespread availability of cameras and a growing need for public safety have shifted the atten...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2016
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| Online Access: | http://psasir.upm.edu.my/id/eprint/35535/1/A%20new%20clustering%20approach%20for%20group%20detection%20in%20scene-independent%20dense%20crowds.pdf |
| _version_ | 1825948665870548992 |
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| author | Wong, Pei Voon Mustapha, Norwati Affendey, Lilly Suriani Khalid, Fatimah |
| author_facet | Wong, Pei Voon Mustapha, Norwati Affendey, Lilly Suriani Khalid, Fatimah |
| author_sort | Wong, Pei Voon |
| collection | UPM |
| description | Despite significant progress in crowd behaviour analysis over the past few years, most of today's state of the art algorithms focus on analysing individual behaviour in a specific-scene. Recently, the widespread availability of cameras and a growing need for public safety have shifted the attention of researchers in video surveillance from individual behavior analysis to group and crowd behavior analysis. However, dangerous and illegal behaviours are mostly occurred from groups of people. Group detection is the main process to separate people in crowded scene into different group based on their interactions. Results of group detection can further to apply in analyze group and crowd behaviour. This paper present a study of the group detection and propose a novel approach for clustering group of people in different crowded scenes based on trajectories. For the clustering of group of people we propose novel formula to compute the weights based on the distance, the occurrence, and the speed correlations of two people in a tracklet cluster to infer the people relationship in a tracklet clusters with Expectation Maximization (EM) in order to overcome occlusion in crowded scenes. |
| first_indexed | 2024-03-06T08:32:37Z |
| format | Conference or Workshop Item |
| id | upm.eprints-35535 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T08:32:37Z |
| publishDate | 2016 |
| publisher | IEEE |
| record_format | dspace |
| spelling | upm.eprints-355352018-06-26T03:40:12Z http://psasir.upm.edu.my/id/eprint/35535/ A new clustering approach for group detection in scene-independent dense crowds Wong, Pei Voon Mustapha, Norwati Affendey, Lilly Suriani Khalid, Fatimah Despite significant progress in crowd behaviour analysis over the past few years, most of today's state of the art algorithms focus on analysing individual behaviour in a specific-scene. Recently, the widespread availability of cameras and a growing need for public safety have shifted the attention of researchers in video surveillance from individual behavior analysis to group and crowd behavior analysis. However, dangerous and illegal behaviours are mostly occurred from groups of people. Group detection is the main process to separate people in crowded scene into different group based on their interactions. Results of group detection can further to apply in analyze group and crowd behaviour. This paper present a study of the group detection and propose a novel approach for clustering group of people in different crowded scenes based on trajectories. For the clustering of group of people we propose novel formula to compute the weights based on the distance, the occurrence, and the speed correlations of two people in a tracklet cluster to infer the people relationship in a tracklet clusters with Expectation Maximization (EM) in order to overcome occlusion in crowded scenes. IEEE 2016 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/35535/1/A%20new%20clustering%20approach%20for%20group%20detection%20in%20scene-independent%20dense%20crowds.pdf Wong, Pei Voon and Mustapha, Norwati and Affendey, Lilly Suriani and Khalid, Fatimah (2016) A new clustering approach for group detection in scene-independent dense crowds. In: 2016 3rd International Conference on Computer and Information Sciences (ICCOINS), 15-17 Aug. 2016, Kuala Lumpur, Malaysia. (pp. 414-417). https://ieeexplore.ieee.org/document/7783251/ 10.1109/ICCOINS.2016.7783251 |
| spellingShingle | Wong, Pei Voon Mustapha, Norwati Affendey, Lilly Suriani Khalid, Fatimah A new clustering approach for group detection in scene-independent dense crowds |
| title | A new clustering approach for group detection in scene-independent dense crowds |
| title_full | A new clustering approach for group detection in scene-independent dense crowds |
| title_fullStr | A new clustering approach for group detection in scene-independent dense crowds |
| title_full_unstemmed | A new clustering approach for group detection in scene-independent dense crowds |
| title_short | A new clustering approach for group detection in scene-independent dense crowds |
| title_sort | new clustering approach for group detection in scene independent dense crowds |
| url | http://psasir.upm.edu.my/id/eprint/35535/1/A%20new%20clustering%20approach%20for%20group%20detection%20in%20scene-independent%20dense%20crowds.pdf |
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