Abstract
The analysis of motion crowds is concerned with the detection of potential hazards for individuals of the crowd. Existing methods analyze the statistics of pixel motion to classify non-dangerous or dangerous behavior, to detect outlier motions, or to estimate the mean throughput of people for an image region. We suggest a biologically inspired model for the analysis of motion crowds that extracts motion features indicative for potential dangers in crowd behavior. Our model consists of stages for motion detection, integration, and pattern detection that model functions of the primate primary visual cortex area (V1), the middle temporal area (MT), and the medial superior temporal area (MST), respectively. This model allows for the processing of motion transparency, the appearance of multiple motions in the same visual region, in addition to processing opaque motion. We suggest that motion transparency helps to identify “danger zones” in motion crowds. For instance, motion transparency occurs in small exit passages during evacuation. However, motion transparency occurs also for non-dangerous crowd behavior when people move in opposite directions organized into separate lanes. Our analysis suggests: The combination of motion transparency and a slow motion speed can be used for labeling of candidate regions that contain dangerous behavior. In addition, locally detected decelerations or negative speed gradients of motions are a precursor of danger in crowd behavior as are globally detected motion patterns that show a contraction toward a single point. In sum, motion transparency, image speeds, motion patterns, and speed gradients extracted from visual motion in videos are important features to describe the behavioral state of a motion crowd.
Highlights
Due to the increasing urbanization, motion crowds become more likely and solutions to prevent accidents or potential hazards as a result of mass panics become more important
Our goal is to provide a first module of such a system that can process visual motion and extract motion features, including the case of motion transparency as generated by certain crowd dynamics
In our analysis we describe a subset of all possible dangerous behaviors, that of a jammed flow which corresponds to zero motion and that of merging people streams during an evacuation that corresponds to the combined occurrence of motion transparency and slow motion speed
Summary
Due to the increasing urbanization, motion crowds become more likely and solutions to prevent accidents or potential hazards as a result of mass panics become more important. Designers of public spaces and intelligent environments begin to consider crowd-dynamics and crowd-environment interactions in everyday situations as well as in exceptional situations such as mass panic [1], [2]. Most public spaces are not equipped with an intelligent crowd management system or automated surveillance system, as suggested in [3], [4]. Developed metrics (level of service) for crowd dynamics have been described as free, restricted, dense, and jammed people flow [5], [6]. Research in cognitive science and neuroscience can help to develop assistive tools or an automated analysis of crowd behavior that supports the detection of dangerous behavior. Our goal is to provide a first module of such a system that can process visual motion and extract motion features, including the case of motion transparency as generated by certain crowd dynamics
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