Minor Area Motion Imagery (MAMI) Dismount Tower Data challenge problems

Freeman, Andrew M.; Cain, Lindsay R.; Zelnio, Holly; Watson, Edward; Mendoza-Schrock, Olga

doi:10.1109/naecon.2014.7045806

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“…The MAMI-1 data collection [6] was completed in 2013 by AFRL with the goal of providing a rich research data set lacking the complications introduced by operational sensor platforms, such as image compression and low frame rates. In addition, MAMI-1 provides multiple sensors at different resolutions in both visible and infrared bands.…”

Section: Datasetmentioning

confidence: 99%

Extraction and classification of moving targets in multi-sensory MAMI-1 data collection

Ilin

Clouse

2015

2015 National Aerospace and Electronics Conference (NAECON)

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In this work we consider the problem of extraction and classification of moving targets in wide area imagery. We use the Air Force Research Laboratory's (AFRL) airborne multisensor dataset, MAMI-1, for testing, wherein moving targets mostly consist of people and vehicles. The movers are extracted using a novel sparse and low-rank matrix decomposition technique. We further compare the classification performance based on SIFT, Dense SIFT, and a superpixel based feature extraction. The results show the superpixel approach as the most advantageous.

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Section: Datasetmentioning

confidence: 99%