Hi,
The model outputs an embedding vector that you can match to previously seen embeddings. This is an open-source model, so its accuracy is probably not state-of-the-art.
However, you can add additional heuristics to improve accuracy. The demo itself is not very complicated and uses simple heuristics. It utilizes our bbox-based tracker along with ReID embeddings.
There is no built-in verification of the “quality” of the images from which you obtain the embeddings. Additionally, the “threshold” for recognition can likely be fine-tuned.
You can achieve significantly higher accuracy by incorporating heuristics—for example, if the same person appears in two locations simultaneously, one of those detections is likely incorrect (neglecting quantum effects 
). In such cases, you should prioritize the detection with the higher match accuracy.
Furthermore, you can leverage knowledge of camera locations to enable tracking across different camera sources. Another potential improvement is integrating face recognition, depending on the positioning and resolution of the cameras.
To summarize, there are many ways to improve accuracy, depending on how much effort you’re willing to invest.