It's clearly single-frame; you can watch recognition succeed and fail from frame to frame. The "person" recognizer misses some clear faces, so it's not heavily face-oriented. Since it's single-frame, it's not recognizing articulated motion.

Recognition seems to be limited to "person", "motorbike", "tie", "cell phone" (a gun, actually), "umbrella", "truck" (misidentified part of a train) "bench" (a railing) and "horse" (motorbike with duffel seen from rear). "Person", "umbrella", "tie", and "motorbike" seem to work; the others are kind of random.

The trouble with running recognizers on Hollywood movies is that they have many conventions of what appears on screen and how big it is on screen. Are they training on such data?

Good data sets would be side views from a moving vehicle, like Google StreetView data or just a GoPro pointed sideways while driving around.