Publicación:
Object detection in videos using principal component pursuit and convolutional neural networks
Object detection in videos using principal component pursuit and convolutional neural networks
No hay miniatura disponible
Fecha
2018
Autores
Tejada Gamero, Enrique David
Título de la revista
Revista ISSN
Título del volumen
Editor
Pontificia Universidad Católica del Perú
Proyectos de investigación
Unidades organizativas
Número de la revista
Abstracto
Object 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.
Descripción
Palabras clave
Visión por computadoras,
Reconocimiento de imágenes,
Redes neuronales--Aplicaciones