The rapid advancements in wireless technology and digital electronics have led to the widespread adoption of compact, intelligent devices in various aspects of daily life. These advanced systems possess the capability to sense environmental changes, process data, and communicate seamlessly within interconnected networks. Typically, such devices integrate low-power radio transmitters and multiple smart sensors, hence enabling efficient functionality across wide ranges of applications. Alongside these technological developments, the concept of the IoT has emerged as a transformative paradigm, facilitating the interconnection of uniquely identifiable devices through internet-based networks. This paper aims to provide a comprehensive ex

In recent years, there has been a growing popularity of autonomous vehicles due to their significant impact on society. One of the key tasks of autonomous vehicles is accurate pedestrian detection, which plays a vital role in preventing accidents. However, accurately detecting and tracking pedestrians under various environmental circumstances poses a significant challenge. In this paper, an efficient model for pedestrian detection is proposed by integrating three modules: You Only Look Once version 8 (YOLOv8) for object segmentation, Histogram of Oriented Gradients (HOG) for feature extraction, and Custom Convolutional Neural Network (CNN) for classification and detection. For tracking purposes, a simple online and real-time tracking (SO

Pedestrian detection is well known as one of the most important applications in computer vision. However, reliable pedestrian detection is difficult due to a variety of factors, including changing size of pedestrian characteristics and crowded backgrounds. This study aims to evaluate and compare the pedestrian detection performance of three different types of classifiers: Random-Forest (RF), Convolution-Neural-Network (CNN), and Support-Vector-Machine (SVM). The presented methodology involves using You_Only_Look_Once (YOLOv8) architecture for object segmentation and the Histogram of Oriented Gradients (HOG) for feature extraction. Then, RF, CNN and SVM classifiers are trained and tested using the extracted HOG features.