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Related Experiment Video

Updated: Mar 22, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

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Analysing Domain Shift Factors between Videos and Images for Object Detection.

Vicky Kalogeiton, Vittorio Ferrari, Cordelia Schmid

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |April 13, 2016
    PubMed
    Summary
    This summary is machine-generated.

    Domain shift significantly impacts object detector performance. Four key factors—spatial accuracy, appearance diversity, image quality, and aspect distribution—explain the performance gap between image and video training data.

    Related Experiment Videos

    Last Updated: Mar 22, 2026

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

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    Area of Science:

    • Computer Vision
    • Machine Learning

    Background:

    • Object detection is a core computer vision task.
    • Detectors trained on still images perform differently on video data.
    • This performance gap necessitates understanding domain shift factors.

    Purpose of the Study:

    • Investigate the reasons for performance discrepancies in object detectors.
    • Quantify the impact of specific domain shift factors.
    • Determine the combined effect of these factors on detector performance.

    Main Methods:

    • Defined and evaluated four domain shift factors: spatial location accuracy, appearance diversity, image quality, and aspect distribution.
    • Compared detector performance before and after mitigating these factors.
    • Analyzed the contribution of each factor to the overall performance gap.

    Main Results:

    • All four identified factors (spatial accuracy, appearance diversity, image quality, aspect distribution) significantly impact object detector performance.
    • The combined effect of these factors accounts for the majority of the observed performance gap.
    • Mitigating these factors can improve cross-domain detector performance.

    Conclusions:

    • Domain shift is a critical challenge in object detection.
    • Understanding and addressing spatial accuracy, appearance diversity, image quality, and aspect distribution is crucial for robust detectors.
    • Future work should focus on developing methods to reduce domain shift effects for improved generalization.