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: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/83244/1/FSKTM%202018%2085%20-%20ir.pdf |
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| Summary: | 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. |
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