Automated, invariant object detection has proven itself to be a substantial challenge for the artificial intelligence research community. In computer vision, many different benchmarks have been established using whole-image classification based on datasets that are too small to eliminate statistical artifacts. As an alternative, we used a new dataset consisting of ~62GB (on the order of 40,000 2Mpixel frames) of compressed high-definition aerial video, which we employed for both object classification and localization. Our algorithms mimic the processing pathways in primate visual cortex, exploiting color/texture, shape/form and motion. We then combine the data using a clustering technique to produce a final output in the form of labeled bounding boxes around objects of interest in the video. Localization adds additional complexity not generally found in whole-image classification problems. Our results are evaluated qualitatively and quantitatively using a scoring metric that assessed the overlap between our detections and ground-truth.

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