Crowd Behavior Analysis Using Local Mid-Level Visual Descriptors

Crowd behavior analysis has recently emerged as an increasingly important and dedicated problem for crowd monitoring and management in the visual surveillance community. In particular, it is receiving a lot of attention to detect potentially dangerous situations and to prevent overcrowdedness. In th...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology 2017-03, Vol.27 (3), p.589-602
Main Authors: Fradi, Hajer, Luvison, Bertrand, Pham, Quoc Cuong
Format: Article
Language:English
Subjects:
Anomalies
Anomaly detection
Character recognition
crowd
Crowd monitoring
Datasets
Delaunay triangulation
Engineering Sciences
event classification
Feature extraction
feature tracks
Graphical representations
Histograms
Signal and Image processing
State of the art
Surveillance
Tracking
Trajectory
Video data
Violence
visual descriptors
Visualization
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Summary:Crowd behavior analysis has recently emerged as an increasingly important and dedicated problem for crowd monitoring and management in the visual surveillance community. In particular, it is receiving a lot of attention to detect potentially dangerous situations and to prevent overcrowdedness. In this paper, we propose to quantify crowd properties by a rich set of visual descriptors. The calculation of these descriptors is realized through a novel spatio-temporal model of the crowd. It consists of modeling time-varying dynamics of the crowd using local feature tracks. It also involves a Delaunay triangulation to approximate neighborhood interactions. In total, the crowd is represented as an evolving graph, where the nodes correspond to the tracklets. From this graph, various mid-level representations are extracted to determine the ongoing crowd behaviors. In particular, the effectiveness of the proposed visual descriptors is demonstrated within three applications: crowd video classification, anomaly detection, and violence detection in crowds. The obtained results on videos from different data sets prove the relevance of these visual descriptors to crowd behavior analysis. In addition, by means of comparisons to other existing methods, we demonstrate that the proposed descriptors outperform the state-of-the-art methods with a significant margin using the most challenging data sets.
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2016.2615443