Shadow Casting Out Of Plane (SCOOP) Candidates for Human and Vehicle Detection in Aerial Imagery

    Authors

    V. Reilly; B. Solmaz;M. Shah

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    Abbreviated Journal Title

    Int. J. Comput. Vis.

    Keywords

    Human detection; Vehicle detection; Aerial surveillance; UAV; Shadow; Metadata; Computer Science, Artificial Intelligence

    Abstract

    In this paper, we propose a method for detecting humans and vehicles in imagery taken from a UAV. This is a challenging problem due to a limited number of pixels on target, which makes it more difficult to distinguish objects from background clutter, and results in much larger search space. We propose a method for constraining the search based on a number of geometric constraints obtained from the metadata. Specifically, we obtain the orientation of ground plane normal, the orientation of shadows cast by out of plane objects in the scene, and the relationship between object heights and the size of their corresponding shadows. We use the aforementioned information in a geometry-based shadow, and ground-plane normal blob detector, which provides an initial estimation for locations of shadow casting out of plane (SCOOP) objects in the scene. These SCOOP candidate locations are then classified as either human or clutter using a combination of wavelet features and a Support Vector Machine. To detect vehicles, we similarly find potential vehicle candidates by combining SCOOP and inverted-SCOOP candidates and then classify them using wavelet features and SVM. Our method works on a single frame, and unlike motion detection based methods, it bypasses the entire pipeline of registration, motion detection, and tracking. This method allows for detection of stationary and slowly moving humans and vehicles while avoiding the search across the entire image, allowing accurate and fast localization. We show impressive results on sequences from VIVID and CLIF datasets and provide comparative analysis.

    Journal Title

    International Journal of Computer Vision

    Volume

    101

    Issue/Number

    2

    Publication Date

    1-1-2013

    Document Type

    Article

    Language

    English

    First Page

    350

    Last Page

    366

    WOS Identifier

    WOS:000314291600007

    ISSN

    0920-5691

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