Is there a way to limit an existing TensorFlow object detection model?
Either you finetune your model, with just a few thousands steps, on pedestrians (a small dataset to train would be enough) or you look in your in your label definition file (.pbtx file), search for the person label, and do whatever stuff you want with the others.
On the tensorflow/models repo on GitHub, they supply five pre-trained models for object detection.
These models are trained on the COCO dataset and can identify 90 different objects.
I need a model to just detect people, and nothing else. I can modify the code to only print labels on people, but it will still look for the other 89 objects, which takes more time than just looking for one object.  
I can train my own model, but I would rather be able to use a pre-trained model, instead of spending a lot of time training my own model.
So is there a way, either by modifying the model file, or the TensorFlow or Object Detection API code, so it only looks for a single object?