PoseNet is a deep learning TensorFlow model that allows you to estimate and track human poses (known as "pose estimation") by detecting body parts such as elbows, hips, wrists, knees, and ankles. It uses the joints of these body parts to determine body postures. Nowadays, many industries use this kind of technology in order to improve work efficiency, and in technologies such as augmented reality experiences, animation & gaming, and robotics. The evolution of human-like robots, virtual gaming experiences, motion tracking, and body movement interpretations can be done with the use of these types of high-end PoseNet deep learning models. First, we need to install the dependencies needed for our project.

So what is pose estimation anyway? Pose estimation refers to computer vision techniques that detect human figures in images and video, so that one could determine, for example, where someone's elbow shows up in an image. To be clear, this technology is not recognizing who is in an image -- there is no personal identifiable information associated to pose detection. The algorithm is simply estimating where key body joints are. Ok, and why is this exciting to begin with?