Using tensorflow/Keras, I have built a good model which is currently binary classification. For example, training labels would be images of a person's knees bent or knees not bent.
The idea behind such a model could be using a continuous video feed, and when it detects either knees bent or not, a certain probability would output. It works, however, since it's binary, I have probabilities close to 0 or 1 even though a person is not even in the video feed.
Any ideas on how I can change my model (perhaps multi-class) in over a continuous feed - which could be hundreds of images over a minute would only give me probabilities for those events of knees bent or not? If going multi-class, would I then have to also train all other potential images? That seems close to impossible, even with using transfer learning with ImageNet. Any suggestions?