Object detection in videos using principal component pursuit and convolutional neural networks

dc.contributor.advisorRodríguez Valderrama, Paul Antonio
dc.contributor.authorTejada Gamero, Enrique Davides_ES
dc.date.EmbargoEnd2019-01-01
dc.date.accessioned2018-05-03T16:08:58Zes_ES
dc.date.available2018-05-03T16:08:58Zes_ES
dc.date.created2018es_ES
dc.date.issued2018-05-03es_ES
dc.description.abstractObject recognition in videos is one of the main challenges in computer vision. Several methods have been proposed to achieve this task, such as background subtraction, temporal differencing, optical flow, particle filtering among others. Since the introduction of Convolutonal Neural Networks (CNN) for object detection in the Imagenet Large Scale Visual Recognition Competition (ILSVRC), its use for image detection and classification has increased, becoming the state-of-the-art for such task, being Faster R-CNN the preferred model in the latest ILSVRC challenges. Moreover, the Faster R-CNN model, with minimum modifications, has been succesfully used to detect and classify objects (either static or dynamic) in video sequences; in such setup, the frames of the video are input “as is” i.e. without any pre-processing. In this thesis work we propose to use Robust PCA (RPCA, a.k.a. Principal Component Pursuit, PCP), as a video background modeling pre-processing step, before using the Faster R-CNN model, in order to improve the overall performance of detection and classification of, specifically, the moving objects. We hypothesize that such pre-processing step, which segments the moving objects from the background, would reduce the amount of regions to be analyzed in a given frame and thus (i) improve the classification time and (ii) reduce the error in classification for the dynamic objects present in the video. In particular, we use a fully incremental RPCA / PCP algorithm that is suitable for real-time or on-line processing. Furthermore, we present extensive computational results that were carried out in three different platforms: A high-end server with a Tesla K40m GPU, a desktop with a Tesla K10m GPU and the embedded system Jetson TK1. Our classification results attain competitive or superior performance in terms of Fmeasure, achieving an improvement ranging from 3.7% to 97.2%, with a mean improvement of 22% when the sparse image was used to detect and classify the object with the neural network, while at the same time, reducing the classification time in all architectures by a factor raging between 2% and 25%.es_ES
dc.description.uriTesises_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12404/11982
dc.language.isoenges_ES
dc.publisherPontificia Universidad Católica del Perúes_ES
dc.publisher.countryPEes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/pe/*
dc.subjectReconocimiento de imágeneses_ES
dc.subjectRedes neuronales--Aplicacioneses_ES
dc.subjectVisión por computadorases_ES
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#2.02.05es_ES
dc.titleObject detection in videos using principal component pursuit and convolutional neural networkses_ES
dc.typeinfo:eu-repo/semantics/masterThesises_ES
renati.advisor.dni07754238
renati.discipline613077es_ES
renati.levelhttps://purl.org/pe-repo/renati/level#maestroes_ES
renati.typehttp://purl.org/pe-repo/renati/type#tesises_ES
thesis.degree.disciplineProcesamiento de Señales e Imágenes Digitaleses_ES
thesis.degree.grantorPontificia Universidad Católica del Perú. Escuela de Posgradoes_ES
thesis.degree.levelMaestríaes_ES
thesis.degree.nameMaestro en Procesamiento de Señales e Imágenes Digitales.es_ES

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