Real-Time Full-Body Detection Using Computer Vision: Leveraging OpenCV and MediaPipe
Megha B. Chakole, Shrikant Sontakke, Lokesh Umredkar, Virendra Rathod, Roshan Umate, Sanjay S. Dorle · International Journal of Electrical and Electronics Engineering · 2024
This research includes a complete body detection system created using a computer vision library called OpenCV, which is primarily utilized to work on projects connected to image and video processing. Applications such as smart surveillance, human-machine interface, HMI, and human behavior all need body detection. This paper highlights the challenges that develops encounter while creating these kinds of applications. The challenges are choosing appropriate machine learning models and optimizing system performance. The primary goal of this work is to overcome the obstacles and identify solutions for them. OpenCV is one of the most potent and successful library computer vision tools, along with a few image-processing methods that yield real-time data on human movement and interaction. The focus of this research is a MediaPipe framework that Python developers commonly use to gather data on human interaction and real-time video capture. MediaPipe ensures that these programs run reliably and frees engineers to focus on improving algorithms. This research's finding offers potential human body detection approaches utilizing Mediapipe and OpenCV, which enable precise and comprehensive views of body posture, hand, and facial recognition.