Pedestrian detection with LeNet-like convolutional networks
Fecha
2020-09
Título de la revista
ISSN de la revista
Título del volumen
Editor
Springer Nature
Enlace externo
Resumen
We present a detection method that is able to detect a learned target and is valid for both static and moving cameras. As an application, we detect pedestrians, but could be anything if there is a large set of images of it. The data set is fed into a number of deep convolutional networks, and then, two of these models are set in cascade in order to filter the cutouts of a multi-resolution window that scans the frames in a video sequence. We demonstrate that the excellent performance of deep convolutional networks is very difficult to match when dealing with real problems, and yet we obtain competitive results.
Descripción
Palabras clave
Citación
Cuesta-Infante, A., García, F.J., Pantrigo, J.J. et al. Pedestrian detection with LeNet-like convolutional networks. Neural Comput & Applic 32, 13175–13181 (2020). https://doi.org/10.1007/s00521-017-3197-z