Table 2. Information on videos analysed...

Table 2.

Information on videos analysed in this study

Analysis ID	Train video	Tracking video	Train method	Number of datasets	Training/labelling condition
Case1	BW1-1	BW1-1	M1	540	Fully labelled images
Case2	BW1-1	BW1-1	M2	540	Single-image labelling (copy)
Case3	BW1-1	BW1-1	M3	1000	Partial labelling (B&W intersection)
Case4	BW1-1	BW1-1	M3	1000	Partial labelling (two squares)
Case5	BW1-1	BW1-1	M4	1000	Whole-pattern labelling (expanded set)
Case6	BW1-1	BW1-2	M4	1000	Pre-trained model (same pattern)
Case7	BW1-1	BW1-3	M4	1000	Pre-trained model (same pattern)
Case8	BW1-1	BW2	M4	1000	Pre-trained model (same pattern)
Case9	BW1-1	BW3	M4	1000	Pre-trained model (same pattern)
Case10	BW2	BW2	M4	1000	Self-trained model for comparison with pre-trained cases
Case11	BW3	BW3	M4	1000	Self-trained model for comparison with pre-trained cases
Case12	O1	O1	M4	1000	Pre-trained model applied to different pattern types
Case13	O1	O2	M4	1000	Pre-trained model applied to different pattern types
Case14	O1	O3	M4	1000	Pre-trained model applied to different pattern types
Case15	S1	S1	M4	1000	Pre-trained model applied to different pattern types
Case16	S1	S2	M4	1000	Pre-trained model applied to different pattern types
Case17	S1	S3	M4	1000	Pre-trained model applied to different pattern types
Case18	C1	C1	M4	1000	Pre-trained model applied to different pattern types
Case19	C1	C2	M4	1000	Pre-trained model applied to different pattern types
Case20	C1	C3	M4	1000	Pre-trained model applied to different pattern types
Case21	X1	X1	M4	1000	Pre-trained model applied to different pattern types
Case22	X1	X2	M4	1000	Pre-trained model applied to different pattern types
Case23	X1	X3	M4	1000	Pre-trained model applied to different pattern types

Analysis ID	Train video	Tracking video	Train method	Number of datasets	Training/labelling condition
Case1	BW1-1	BW1-1	M1	540	Fully labelled images
Case2	BW1-1	BW1-1	M2	540	Single-image labelling (copy)
Case3	BW1-1	BW1-1	M3	1000	Partial labelling (B&W intersection)
Case4	BW1-1	BW1-1	M3	1000	Partial labelling (two squares)
Case5	BW1-1	BW1-1	M4	1000	Whole-pattern labelling (expanded set)
Case6	BW1-1	BW1-2	M4	1000	Pre-trained model (same pattern)
Case7	BW1-1	BW1-3	M4	1000	Pre-trained model (same pattern)
Case8	BW1-1	BW2	M4	1000	Pre-trained model (same pattern)
Case9	BW1-1	BW3	M4	1000	Pre-trained model (same pattern)
Case10	BW2	BW2	M4	1000	Self-trained model for comparison with pre-trained cases
Case11	BW3	BW3	M4	1000	Self-trained model for comparison with pre-trained cases
Case12	O1	O1	M4	1000	Pre-trained model applied to different pattern types
Case13	O1	O2	M4	1000	Pre-trained model applied to different pattern types
Case14	O1	O3	M4	1000	Pre-trained model applied to different pattern types
Case15	S1	S1	M4	1000	Pre-trained model applied to different pattern types
Case16	S1	S2	M4	1000	Pre-trained model applied to different pattern types
Case17	S1	S3	M4	1000	Pre-trained model applied to different pattern types
Case18	C1	C1	M4	1000	Pre-trained model applied to different pattern types
Case19	C1	C2	M4	1000	Pre-trained model applied to different pattern types
Case20	C1	C3	M4	1000	Pre-trained model applied to different pattern types
Case21	X1	X1	M4	1000	Pre-trained model applied to different pattern types
Case22	X1	X2	M4	1000	Pre-trained model applied to different pattern types
Case23	X1	X3	M4	1000	Pre-trained model applied to different pattern types

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