Research Communication | Open Access
Volume 2021 | Communication ID 28
Volume 2021 | Communication ID 28
| Pedestrian Detection based on Neural Networks |
Mohammed Razzok, Abdelmajid Badri, Ilham El Mourabit, Yassine Ruichek, Aïcha Sahel
Received |
Accepted |
Published |
January 17, 2021 |
January 31, 2021 |
March 15, 2021 |
Abstract: Pedestrian detection is a rapidly developing field of computer vision with several applications in smart cars, surveillance, automotive safety, and advanced robotics. Most of the success of the last few years has been driven by the rapid growth of deep learning, more efficient tools capable of learning semantic, high-level, deeper features of images are proposed. In this paper, we investigated the task of pedestrian detection using convolutional neural network models, and we compared the performance of the standard state-of-the-art object detectors trained using COCO database such as R-CNN, ...