Visual analysis of dense crowds / Kok Ven Jyn
The steady worldwide population growth with continuing urbanization renders the formation of crowd by chance a norm. The mere existence of crowd has the prospect of progressing into a hazardous scene. Consequently, visual analysis of dense crowds is a growing research topic in the domain of compu...
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| Format: | Thesis |
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2016
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| Online Access: | http://studentsrepo.um.edu.my/6773/ http://studentsrepo.um.edu.my/6773/4/ven_jyn.pdf |
| Summary: | The steady worldwide population growth with continuing urbanization renders the
formation of crowd by chance a norm. The mere existence of crowd has the prospect of
progressing into a hazardous scene. Consequently, visual analysis of dense crowds is a
growing research topic in the domain of computer vision. Conventional visual analysis
methods are mostly object-centric, thus, are neither suitable nor capable of analyzing
dense crowd. Hence, this thesis proposes novel solutions to analyze images and videos of
dense crowds, which contain hundreds to thousands of individuals. The main objective
are, first, to obviate the difficulty of segregating individuals in dense crowd scenes to
infer dense crowd segments, secondly to estimate the number of individuals and finally to
detect unusual events, by exploiting spatial and temporal cues readily available from the
scenes.
Dense crowd segmentation generally serves as one of the essential steps for further
visual analysis of the dense crowds. The thesis first demonstrates the significance
of simplifying dense crowd scenes into structurally meaningful atomic regions for dense
crowd segmentation. This proposed approach is formulated using the concept and principles
of granular computing. It shows that by exploiting the correlation among pixel
granules, structurally similar pixels can be aggregated into meaningful atomic structure
granules. This is useful in outlining natural boundaries between crowd and background
(i.e. non-crowd) regions necessary for dense crowd segmentation. Moreover, the proposed
approach is scene-independent; thus it can be applied effectively to dense crowd
scenes with a variety of physical layout and crowdedness.
Second, this thesis presents an approach to utilize irregular patches conforming to
the natural outline between crowd and background to estimate the number of individuals
in dense crowd scenes. As opposed to most of the existing approaches that uses pixel-grid representation, the proposed density estimation approach allows a model to adapt itself
to the arbitrary distribution of crowd where the underlying spatial information of scenes
can be accurately extracted. Here, a direct mapping is established between the extracted
features and the number of people.
Third, to detect saliency in dense crowd scenes, low-level features extracted from the
crowd motion field are transformed into a global similarity structure. This global similarity
structure representation allows the discovery of the intrinsic manifold of the motion
dynamics, which could not be captured by the low-level representation. Most importantly,
unlike conventional methods, the proposed approach does not require tracking, and prior
information or model learning to identify interesting / salient regions in the dense crowd
scenes.
These proposed approaches are validated by using public dataset of dense crowd
scenes. From the empirical results, it is anticipated that the collective analysis of this
thesis will constitute a complete dense crowd analysis system that is able to infer regions
of dense crowds, estimate crowd density and identify saliency in mass gathering for
proactive crowd management. |
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