Basic Template matching has severe limitations around scaling, rotation, and perspective. In my experience it greatly underperforms compared to deep network object detectors. My experience- and I imagine others have different experiences- is that SIFT techniques also fail pretty badly with noisy data.

That's correct, and I was specifically referring to the example chosen - where scale and perspective are known. Template rotation is relatively easy as well - but partial obstructions would pose a problem.

Another application where template matching would work brilliantly? Car counting in parking lots using satellite imagery.

Source: I did this [1] using OpenCV and template matching. Outperformed "Cars Overhead with Context" models.

https://abcnews.com/International/satellite-data-suggests-co...