Cuesta-Infante, AlfredoGarcía, Francisco J.Pantrigo, Juan J.S. Montemayor, Antonio2025-01-302025-01-302020-09Cuesta-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-z0941-06431433-3058https://hdl.handle.net/10115/72177We 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.en-USPedestrian detectorsComputer visionLeNet convolutional networksImage classificationArticle10.1007/s00521-017-3197-zinfo:eu-repo/semantics/closedAccess