I asked a similar question, but like you i would like to understand in more detail.
This was @pshin answer
NvDCF tracker color features (e.g., ColorNames) and directional edge features (e.g., HOG) and uses correlation filter to localize the target. The visual similarity score reflects how much the tracker believes the target is at a particular location. When the background is cluttered and has similar features, it is possible that the tracker can yield a high confidence on a non-target location.
In my tests, i’ve noticed it turning off colorspace is beneficial when using the camera is in IR mode (at night). Also ive noticed that wearing a white shirt, with a white background can cause unique_id switching issues when the colorspace on.
As for the hog classifier, i’m not completely sure. It is slightly frustrating not knowing the circumstances that these options perform best in. For now i guess we need to just share our experiences with the trackers behavior to fill in the knowledge gap. I’m sure the documentation will be updated soon to answer some of our questions.