Classification of moving objects in atmospherically degraded video

Eli Chen, Oren Haik, Yitzhak Yitzhaky

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Classification of moving objects in imaging through longdistance atmospheric path may be affected by distortions such as blur and spatiotemporal movements caused by air turbulence. This work aims to study and quantify the effects of these distortions on the ability to classify moving objects in atmospherically degraded video signals. For this purpose, we perform simulations and examine real long-range thermal video cases. In the simulation, we evaluate various geometrical (shape-based) object features for classification at different distortion levels. Furthermore, we examine the influence of image restoration on the classification performances in the real-degraded videos, using geometrical and textural features (combined and in separate) of the objects. Principal component analysis together with both k-nearest neighbor and support vector machines is used for the classification process. Results show how classification performances decrease as the level of blur increases, and how successful digital image restoration for real cases can significantly improve the classification performances.

    Original languageAmerican English
    Article number101710
    JournalOptical Engineering
    Volume51
    Issue number10
    DOIs
    StatePublished - 1 Oct 2012

    Keywords

    • Blind deconvolution
    • Long-distance imaging
    • Object classification
    • Pattern recognition
    • Target tracking
    • Thermal imaging

    All Science Journal Classification (ASJC) codes

    • General Engineering
    • Atomic and Molecular Physics, and Optics

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