Pedestrian detection system based on deep learning

Abstract

<p>Pedestrian detection is a rapidly growing field of computer vision with 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 article, we investigated the task of pedestrian detection on roads using models based on convolutional neural networks. We compared the performance of standard state-of-the-art object detectors like Faster region-based convolutional network (R-CNN), single shot detector (SSD), and you only look once, version 3 (YOLOv3). Results show that YOLOv3 is the best object detection model than others for pedestrians in terms of detection and time prediction.</p>

Integrating YOLOv8-agri and DeepSORT for Advanced Motion Detection in Agriculture and Fisheries

“…Research on pedestrian tracking using YOLOv5 in conjunction with DeepSORT reveals the algorithms' effectiveness in handling challenges like sudden movement, occlusion, and appearance changes in real-time scenarios. These studies highlight the potential of these technologies in autonomous driving and intelligent surveillance, where accurate pedestrian detection and tracking are crucial [21,22].…”

Pedestrian detection under weather conditions using conditional generative adversarial network

“…As a result, pedestrian detection algorithms, which find all pedestrians in an image, have gained popularity in computer vision and artificial intelligence communities. Common pedestrian detection systems (PDS) are built for bright weather [1]- [9]. However, an applicable PDS is also required to perform well in rainy or snowy conditions.…”

“…Machine learning (ML) field focuses on the development of computer algorithms, which exploit data to learn patterns, make predictions, and increase their performance over time by more data. Lately, taking advantage of the invention of convolutional neural networks [12]- [15], particularly the establishment of the pix2pix [16] network architecture and the adversarial training strategy, the performance of single image deraining has experienced notable progress. By training a rainy-to-clean image translation model with synthetic rain streak or raindrop datasets, a rainy image can be effectively repaired by eliminating the artifacts despite the presence of rain streaks or raindrops with different scales, forms, and thicknesses.…”