Crowd behaviour monitoring

ABSTRACT

We present a tool that automatically detects abnormal situations in crowded scenes in real time. The followed approach analyzes the general motion aspect, instead of tracking subjects one by one, by detecting abnormal optical flow patterns of tracked KLT points. The number of tracked points is reduced by using a learned mask. We define a measure that describes the situation abnormality based on crowd density, direction variance and distribution, mean velocity and sometimes trajectory matching. To demonstrate the interest of this approach, we present the results on the detection of collapsing events in real videos of airport escalator exits.

REFERENCES

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.10 Vision and Scene Understanding
            Subjects: Motion

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.10 Vision and Scene Understanding
            Subjects: Video analysis

General Terms:
Algorithms, Security

Keywords:
crowd behavior, event detection, video surveillance