Table 2. Results of the human validation for a subset of videos.

Validation was performed by going through all problematic situations (e.g. individuals lost) and correcting mistakes manually, creating a fully corrected dataset for the given videos. This dataset may still have missing frames for some individuals, if they could not be detected in certain frames (as indicated by ‘of that interpolated’). This was usually a very low percentage of all frames, except for Video 9, where individuals tended to rest on top of each other – and were thus not tracked – for extended periods of time. This baseline dataset was compared to all other results obtained using the automatic visual identification by TRex ($N=5$) and idtracker.ai ($N=3$) to estimate correctness. We were not able to track Videos 9 and 10 with idtracker.ai, which is why correctness values are not available.

Video metrics		Review stats		% correct
Video	# ind.	Reviewed (%)	Of that interpolated (%)	TRex	idtracker.ai
7	100	100.0	0.23	99.07 ± 0.013	98.95 ± 0.146
8	59	100.0	0.15	99.68 ± 0.533	99.94 ± 0.0
9	15	22.2	8.44	95.12 ± 6.077	N/A
10	10	100.0	1.21	99.7 ± 0.088	N/A
13	10	100.0	0.27	99.98 ± 0.0	99.96 ± 0.0
12	10	100.0	0.59	99.94 ± 0.006	99.63 ± 0.0
11	10	100.0	0.5	99.89 ± 0.009	99.34 ± 0.002