HIGH-PRECISION CROWD TRACKING AND MAPPING USING MODERN NEURAL NETWORK ARCHITECTURES

Crowd persecution and mapping with high accuracy using modern architectures of neuronal networks is a modernoverview, and innovations in the architecture of neuronal networks have significantly improved the capabilities ofcrowd tracking and mapping. Object recognition forms fundamental efforts in the field of computer vision, which iswidely used in various sectors, including self-driving cars, surveillance, healthcare, and industrial automation. Thismanuscript introduces an optimized and optimized implementation of Yolov8 object detection frames. It is integratedinto OPENCV for real-time performance. The system uses the coconut reservation model to accurately identifyseveral object categories with considerable accuracy. Include threshold mechanisms to improve accuracy andsimultaneously improve computational efficiency. Through comprehensive experiments, our methodology hasexcellent effectiveness in real-time object recognition and achieves a high level of accuracy with negligible latency.This study contributes to a robust solution for visual-based applications that function in real time to ensure bothcomputational efficiency and model reliability.