Wide area motion imagery (WAMI) has been attracting an increased amount of research attention due to its large spatial and temporal coverage. An important application includes moving target analysis, where vehicle detection is often one of the first steps before advanced activity analysis. While there exist many vehicle detection algorithms, a thorough evaluation of them on WAMI data still remains a challenge mainly due to the lack of an appropriate benchmark data set. In this paper, we address a research need by presenting a new benchmark for wide area motion imagery vehicle detection data. The WAMI benchmark is based on the recently available Wright-Patterson Air Force Base (WPAFB09) dataset and the Temple Resolved Uncertainty Target History (TRUTH) associated target annotation. Trajectory annotations were provided in the original release of the WPAFB09 dataset, but detailed vehicle annotations were not available with the dataset. In addition, annotations of static vehicles, e.g., in parking lots, are also not identified in the original release. Addressing these issues, we re-annotated the whole dataset with detailed information for each vehicle, including not only a target’s location, but also its pose and size. The annotated WAMI data set should be useful to community for a common benchmark to compare WAMI detection, tracking, and identification methods.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A benchmark for vehicle detection on wide area motion imagery


    Contributors:

    Conference:

    Sensors and Systems for Space Applications VIII ; 2015 ; Baltimore,Maryland,United States


    Published in:

    Publication date :

    2015-05-22





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Detection and tracking performance with compressed wide area motion imagery

    Hytla, Patrick C. / Jackovitz, Kevin S. / Balster, Eric J. et al. | IEEE | 2012


    Exelis aims to export wide-area motion Imagery System

    Warwick, Graham | Online Contents | 2014


    Low resolution vehicle re-identification based on appearance features for wide area motion imagery

    Cormier, M. / Sommer, L. / Teutsch, Michael | BASE | 2016

    Free access

    The URREF ontology for semantic wide area motion imagery exploitation

    Blasch, Erik / Costa, Paulo C. G. / Laskey, Kathryn B. et al. | IEEE | 2012