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    This study introduces a novel method for transforming multi-perspective spatiotemporal activity maps into a unified orthographic view. This enables consistent, wide-area comparisons of motion patterns and spatial probability estimation.

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    Area of Science:

    • Computer Vision
    • Data Visualization
    • Spatial Analysis

    Background:

    • Spatiotemporal activity maps visualize motion over time, typically as heat maps.
    • Current methods lack consistent comparison across different camera viewpoints.
    • Integrating data from multiple perspectives is challenging.

    Purpose of the Study:

    • To develop a mapping technique for unifying activity maps from diverse camera perspectives into a single orthographic view.
    • To enable consistent data comparison for wide-area analysis.
    • To minimize manual camera calibration through statistically learned perspectives.

    Main Methods:

    • A set of novel mappings to transform multi-perspective activity maps.
    • Statistical learning of camera perspectives to automate calibration.
    • Application of mappings to various video datasets.

    Main Results:

    • Successful transformation of activity maps to a consistent orthographic perspective.
    • Demonstrated ability to view and compare data from multiple camera views.
    • Validation of statistically learned perspectives for reduced calibration effort.

    Conclusions:

    • The developed mapping technique facilitates unified spatiotemporal data analysis.
    • Enables consistent visualization and comparison of activity across wide areas.
    • Applications include enhanced spatial probability estimation and data visualization.