11,991 research outputs found
Texture-based crowd detection and localisation
This paper presents a crowd detection system based on texture analysis. The state-of-the-art techniques based on co-occurrence matrix have been revisited and a novel set of features proposed. These features provide a richer description of the co-occurrence matrix, and can be exploited to obtain stronger classification results, especially when smaller portions of the image are considered. This is extremely useful for crowd localisation: acquired images are divided into smaller regions in order to perform a classification on each one. A thorough evaluation of the proposed system on a real world data set is also presented: this validates the improvements in reliability of the crowd detection and localisation
Crowd Abnormal Behaviour Detection and Analysis
The analysis and understanding of abnormal behaviours in human crowds is a challenging task in pattern recognition and computer vision. First of all, the semantic definition of the term “crowd” is ambiguous. Secondly, the taxonomy of crowd behaviours is usually rudimentary and intrinsically complicated. How to identify and construct effective features for crowd behaviour classification is a prominent challenge. Thirdly, the acquisition of suitable video for crowd analysis is another critical problem.
In order to address those issues, a categorization model for abnormal behaviour types is defined according to the state-of-the-art. In the novel taxonomy of crowd behaviour, eight types of crowd behaviours are defined based on the key visual patterns. An enhanced social force-based model is proposed to achieve the visual realism in crowd simulation, hence to generate customizable videos for crowd analysis. The proposed model consists of a long-term behavior control model based on A-star path finding algorithm and a short-term interaction handling model based on the enhanced social force. The proposed simulation approach produced all the crowd behaviours in the new taxonomy for the training and testing of the detection procedure. On the aspect of feature engineering, an innovative signature is devised for assisting the segmentation of crowd in both low and high density. The signature is modelled with derived features from Grey-Level Co-occurrence Matrix. Another major breakthrough is an effective approach for efficiently extracting spatial temporal information based on the information entropy theory and Gabor background subtraction. The extraction approach is capable of obtaining the texture with most motion information, which could help the detection approach to achieve the real-time processing.
Overall, these contributions have supported the crucial components in a pipeline of abnormal crowd behaviour detecting process. This process is consisted of crowd behaviour taxonomy, crowd video generation, crowd segmentation and crowd abnormal behaviour detection. Experiments for each component show promising results, and proved the accessibility of the proposed approaches
Combining Multiple Clusterings via Crowd Agreement Estimation and Multi-Granularity Link Analysis
The clustering ensemble technique aims to combine multiple clusterings into a
probably better and more robust clustering and has been receiving an increasing
attention in recent years. There are mainly two aspects of limitations in the
existing clustering ensemble approaches. Firstly, many approaches lack the
ability to weight the base clusterings without access to the original data and
can be affected significantly by the low-quality, or even ill clusterings.
Secondly, they generally focus on the instance level or cluster level in the
ensemble system and fail to integrate multi-granularity cues into a unified
model. To address these two limitations, this paper proposes to solve the
clustering ensemble problem via crowd agreement estimation and
multi-granularity link analysis. We present the normalized crowd agreement
index (NCAI) to evaluate the quality of base clusterings in an unsupervised
manner and thus weight the base clusterings in accordance with their clustering
validity. To explore the relationship between clusters, the source aware
connected triple (SACT) similarity is introduced with regard to their common
neighbors and the source reliability. Based on NCAI and multi-granularity
information collected among base clusterings, clusters, and data instances, we
further propose two novel consensus functions, termed weighted evidence
accumulation clustering (WEAC) and graph partitioning with multi-granularity
link analysis (GP-MGLA) respectively. The experiments are conducted on eight
real-world datasets. The experimental results demonstrate the effectiveness and
robustness of the proposed methods.Comment: The MATLAB source code of this work is available at:
https://www.researchgate.net/publication/28197031
Ground truth? Concept-based communities versus the external classification of physics manuscripts
Community detection techniques are widely used to infer hidden structures
within interconnected systems. Despite demonstrating high accuracy on
benchmarks, they reproduce the external classification for many real-world
systems with a significant level of discrepancy. A widely accepted reason
behind such outcome is the unavoidable loss of non-topological information
(such as node attributes) encountered when the original complex system is
represented as a network. In this article we emphasize that the observed
discrepancies may also be caused by a different reason: the external
classification itself. For this end we use scientific publication data which i)
exhibit a well defined modular structure and ii) hold an expert-made
classification of research articles. Having represented the articles and the
extracted scientific concepts both as a bipartite network and as its unipartite
projection, we applied modularity optimization to uncover the inner thematic
structure. The resulting clusters are shown to partly reflect the author-made
classification, although some significant discrepancies are observed. A
detailed analysis of these discrepancies shows that they carry essential
information about the system, mainly related to the use of similar techniques
and methods across different (sub)disciplines, that is otherwise omitted when
only the external classification is considered.Comment: 15 pages, 2 figure
On Pairwise Costs for Network Flow Multi-Object Tracking
Multi-object tracking has been recently approached with the min-cost network
flow optimization techniques. Such methods simultaneously resolve multiple
object tracks in a video and enable modeling of dependencies among tracks.
Min-cost network flow methods also fit well within the "tracking-by-detection"
paradigm where object trajectories are obtained by connecting per-frame outputs
of an object detector. Object detectors, however, often fail due to occlusions
and clutter in the video. To cope with such situations, we propose to add
pairwise costs to the min-cost network flow framework. While integer solutions
to such a problem become NP-hard, we design a convex relaxation solution with
an efficient rounding heuristic which empirically gives certificates of small
suboptimality. We evaluate two particular types of pairwise costs and
demonstrate improvements over recent tracking methods in real-world video
sequences
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