Using Binary Image Classification on Video
Using a multi-class network would probably be the most efficient approach.

Training two networks (as suggested in the comments: one network to detect the presence of a person and one to detect the state of the knee) is unnecessary and overly complex.  You can train the network on three classes: bent knee, straight knee, absent knee.  For the absent knee, you do not need to train on all possible images, just images you expect to see.  For instance, these negative data could be the background of your intended images; take a picture with the knee and one without.

If speed is a major requirement, the model architecture becomes critical.  I suggest exploring MobileNets as they are quite fast and do not sacrifice too much accuracy.