Collective interaction filtering with graph-based descriptors for crowd behaviour analysis

Crowd behaviour analysis plays an important role in high security interests in public areas such as railway stations, shopping centres, and airports, where large populations gather. Crowd behaviour analysis framework can be divided into low-level, mid-level and high-level. This research is focuse...

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Main Author:	Wong, Pei Voon
Format:	Thesis
Language:	English
Published:	2018
Subjects:	Computer vision Crowds electronic data processing
Online Access:	http://psasir.upm.edu.my/id/eprint/83244/1/FSKTM%202018%2085%20-%20ir.pdf
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spelling	my-upm-ir.832442022-01-07T07:43:02Z Collective interaction filtering with graph-based descriptors for crowd behaviour analysis 2018-12 Wong, Pei Voon Crowd behaviour analysis plays an important role in high security interests in public areas such as railway stations, shopping centres, and airports, where large populations gather. Crowd behaviour analysis framework can be divided into low-level, mid-level and high-level. This research is focused on problems of mid-level and high-level. The crowded scenes vary in various densities, structures and occlusion. It brings enormous challenges in effectively dividing detection feature points into cluster to develop dynamic group detector and grouping consistency between frames at mid-level. Besides that, it also poses challenges in identifying generic descriptors to describe motion dynamics caused by pedestrians walk in different directions with extremely diverse behaviours at high-level. Therefore, crowd behaviour analysis framework with enhanced mid and high levels approaches is used in this research to recognise the common properties across different crowded scenes. The recognised common properties are then used to identify generic descriptors from group-level for crowd behaviour classification and crowd video retrieval. At the low-level, motion feature extraction is performed to extract trajectories from each of the video frames. Kanade-Lucas-Tomasi feature point tracker is used to detect and track moving humans, and then tracklets are grouped to form trajectories. At the mid-level, a Collective Interaction Filtering is presented to identify groups by clustering trajectories. It is suitable for group detection in low, medium, and high crowds. At the high-level, the result of Collective Interaction Filtering is used in group motion pattern mining to predict collectiveness, uniformity, stability, and conflict generic descriptors. The generic descriptors identified are represented by graph-based descriptors. Graph-based descriptors are applied to crowd behaviour analysis and crowd video retrieval. All experiments are carried out using CUHK Crowd dataset. The group detection and crowd behaviour analysis ground truth results were provided by related work. The group detection experiment is implemented using the clustering algorithm. Normalized Mutual Information and Rand Index are used to measure the performance of Collective Interaction Filtering. The crowd behaviour analysis experiment is implemented by using non-linear Structural Support Vector Machine with RBF-kernel classifier. Leave-one-out is used to measure the performance of the proposed graph-based descriptors to describe crowd behaviour. The proposed crowd video retrieval approach based on generic descriptors experiment is implemented by using Euclidean distance and Chi-Square distance to measure the similarity matching generic descriptors between the query video and the retrieval set of videos. The crowd video retrieval performance is measured by the average precision in the top k retrieved samples. Experimental results show that the crowd behaviour analysis framework achieves the state-of-the-art performance on the CUHK Crowd dataset. The Collective Interaction Filtering outperforms the related work by achieving 0.55 for Normalized Mutual Information and 0.83 for Rand Index. The average accuracy of the proposed graph-based descriptors for crowd behaviour analysis is 80% compared to the previous works. The proposed crowd video retrieval approach based on graphbased descriptors obtained 49% in average top 10 precision. The performance improvement reveals the effectiveness of the graph-based descriptors for crowd video retrieval in different crowded scenes. Computer vision Crowds electronic data processing 2018-12 Thesis http://psasir.upm.edu.my/id/eprint/83244/ http://psasir.upm.edu.my/id/eprint/83244/1/FSKTM%202018%2085%20-%20ir.pdf text en public doctoral Universiti Putra Malaysia Computer vision Crowds electronic data processing Mustapha, Norwati
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
advisor	Mustapha, Norwati
topic	Computer vision Crowds electronic data processing
spellingShingle	Computer vision Crowds electronic data processing Wong, Pei Voon Collective interaction filtering with graph-based descriptors for crowd behaviour analysis
description	Crowd behaviour analysis plays an important role in high security interests in public areas such as railway stations, shopping centres, and airports, where large populations gather. Crowd behaviour analysis framework can be divided into low-level, mid-level and high-level. This research is focused on problems of mid-level and high-level. The crowded scenes vary in various densities, structures and occlusion. It brings enormous challenges in effectively dividing detection feature points into cluster to develop dynamic group detector and grouping consistency between frames at mid-level. Besides that, it also poses challenges in identifying generic descriptors to describe motion dynamics caused by pedestrians walk in different directions with extremely diverse behaviours at high-level. Therefore, crowd behaviour analysis framework with enhanced mid and high levels approaches is used in this research to recognise the common properties across different crowded scenes. The recognised common properties are then used to identify generic descriptors from group-level for crowd behaviour classification and crowd video retrieval. At the low-level, motion feature extraction is performed to extract trajectories from each of the video frames. Kanade-Lucas-Tomasi feature point tracker is used to detect and track moving humans, and then tracklets are grouped to form trajectories. At the mid-level, a Collective Interaction Filtering is presented to identify groups by clustering trajectories. It is suitable for group detection in low, medium, and high crowds. At the high-level, the result of Collective Interaction Filtering is used in group motion pattern mining to predict collectiveness, uniformity, stability, and conflict generic descriptors. The generic descriptors identified are represented by graph-based descriptors. Graph-based descriptors are applied to crowd behaviour analysis and crowd video retrieval. All experiments are carried out using CUHK Crowd dataset. The group detection and crowd behaviour analysis ground truth results were provided by related work. The group detection experiment is implemented using the clustering algorithm. Normalized Mutual Information and Rand Index are used to measure the performance of Collective Interaction Filtering. The crowd behaviour analysis experiment is implemented by using non-linear Structural Support Vector Machine with RBF-kernel classifier. Leave-one-out is used to measure the performance of the proposed graph-based descriptors to describe crowd behaviour. The proposed crowd video retrieval approach based on generic descriptors experiment is implemented by using Euclidean distance and Chi-Square distance to measure the similarity matching generic descriptors between the query video and the retrieval set of videos. The crowd video retrieval performance is measured by the average precision in the top k retrieved samples. Experimental results show that the crowd behaviour analysis framework achieves the state-of-the-art performance on the CUHK Crowd dataset. The Collective Interaction Filtering outperforms the related work by achieving 0.55 for Normalized Mutual Information and 0.83 for Rand Index. The average accuracy of the proposed graph-based descriptors for crowd behaviour analysis is 80% compared to the previous works. The proposed crowd video retrieval approach based on graphbased descriptors obtained 49% in average top 10 precision. The performance improvement reveals the effectiveness of the graph-based descriptors for crowd video retrieval in different crowded scenes.
format	Thesis
qualification_level	Doctorate
author	Wong, Pei Voon
author_facet	Wong, Pei Voon
author_sort	Wong, Pei Voon
title	Collective interaction filtering with graph-based descriptors for crowd behaviour analysis
title_short	Collective interaction filtering with graph-based descriptors for crowd behaviour analysis
title_full	Collective interaction filtering with graph-based descriptors for crowd behaviour analysis
title_fullStr	Collective interaction filtering with graph-based descriptors for crowd behaviour analysis
title_full_unstemmed	Collective interaction filtering with graph-based descriptors for crowd behaviour analysis
title_sort	collective interaction filtering with graph-based descriptors for crowd behaviour analysis
granting_institution	Universiti Putra Malaysia
publishDate	2018
url	http://psasir.upm.edu.my/id/eprint/83244/1/FSKTM%202018%2085%20-%20ir.pdf
_version_	1747813364710506496

Collective interaction filtering with graph-based descriptors for crowd behaviour analysis

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