Human Detector & Counting

Human detection and counting are crucial tasks in computer vision, with applications in security surveillance, crowd monitoring, retail analytics, and smart city planning. This paper explores various approaches to human detection and counting, including traditional image processing techniques and modern deep learning-based methods such as Convolutional Neural Networks (CNNs) and You Only Look Once (YOLO). Challenges such as occlusion, varying lighting conditions, and real-time processing constraints are addressed. The study also highlights the integration of human detection models with edge computing and IoT devices to improve efficiency and scalability. Experimental results demonstrate the effectiveness of deep learning models in achieving high accuracy and real-time performance.