Real-time vehicle detection and tracking in video surveillance cameras
Fecha
2020Autor
Director
Versión
Acceso abierto / Sarbide irekia
Tipo
Trabajo Fin de Grado/Gradu Amaierako Lana
Impacto
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nodoi-noplumx
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Resumen
This End of Degree Work is directed to the field of Artificial Intelligence, expressly it is focused
on the study of the Deep Learning techniques. Particularly, convolutional neural networks are
studied with the aim of developing a vehicle detection model that is able to recognize them in
images and videos. YOLOV3 is the vehicle detection model that has been studied, trained and
optimized all ...
[++]
This End of Degree Work is directed to the field of Artificial Intelligence, expressly it is focused
on the study of the Deep Learning techniques. Particularly, convolutional neural networks are
studied with the aim of developing a vehicle detection model that is able to recognize them in
images and videos. YOLOV3 is the vehicle detection model that has been studied, trained and
optimized all through this work, in order to develop a model capable of detecting cars, trucks
and motors in real time. Finally, SORT tracking algorithm has been studied and joint with the
YOLOV3 detection model into a final model which will be responsible of detecting and tracking
vehicles all through different videos in real time. [--]
Materias
Machine learning,
Deep learning,
Convolutional neural network,
Real-time object detection,
Real-time object tracking,
Multiple object tracking,
Traffic estimation
Titulación
Graduado o Graduada en Ingeniería Informática por la Universidad Pública de Navarra /
Informatika Ingeniaritzako Graduatua Nafarroako Unibertsitate Publikoan