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dc.contributor.advisorKölsch, Mathias
dc.contributor.authorDowns, Justin
dc.dateMar-17
dc.date.accessioned2017-05-10T16:31:32Z
dc.date.available2017-05-10T16:31:32Z
dc.date.issued2017-03
dc.identifier.urihttp://hdl.handle.net/10945/52976
dc.descriptionApproved for public release; distribution is unlimiteden_US
dc.description.abstractGiven the problem of detecting objects in video, existing neural-network solutions rely on a post-processing step to combine information across frames and strengthen conclusions. This technique has been successful for videos with simple, dominant objects but it cannot detect objects if a single frame does not contain enough information to distinguish the object from its background. This problem is especially relevant in the maritime environment, where a whitecap and a human survivor may look identical except for their movement through the scene. In order to evaluate a neural network's ability to combine information across multiple frames of information, we developed two versions of a convolutional neural network: one version was given multiple frames as input while the other version was only provided a single frame. We measured the performance of both versions on the benchmark 3DPeS Dataset and observed a significant increase in both recall and precision when the network was given 10 frames instead of just one.We also developed our own noisy dataset consisting of small birds flying across the Monterey Bay. This dataset contained many instances where, in a single frame, the objects to be detected were indistinguishable from the surrounding waves and debris. For this dataset, multiple frames were essential for reliable detections. We also observed a greater improvement in the false negative rate compared to the false positive rate in this noisier dataset, suggesting that the additional frames were especially useful for improving the detection of hard-to-detect objects.en_US
dc.description.urihttp://archive.org/details/multiframeconvol1094552976
dc.publisherMonterey, California: Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleMulti-frame convolutional neural networks for object detection in temporal dataen_US
dc.typeThesisen_US
dc.contributor.secondreaderWhitaker, Lyn
dc.contributor.departmentComputer Science (CS)
dc.subject.authorConvolutional neural networksen_US
dc.subject.authormachine learningen_US
dc.subject.authorobject detectionen_US
dc.subject.authorcomputer visionen_US
dc.description.recognitionOutstanding Thesis
dc.description.serviceLieutenant, United States Navyen_US
etd.thesisdegree.nameMaster of Science in Computer Scienceen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineComputer Scienceen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US


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