A hybrid framework combining background subtraction and deep neural networks for rapid person detection

Journal of Big Data

Table 6 Evaluation with different test datasets

Test set	Input	Precision		Recall		Accuracy
Test set	Input	Person	Non-pers.	Person	Non-pers.	Accuracy
(a) Full-set model
Roadset1	32 × 32	0.78	0.65	0.73	0.71	0.72
	64 × 64	0.79	0.64	0.70	0.73	0.71
	128 × 128	0.79	0.63	0.69	0.75	0.71
Roadset2	32 × 32	0.84	0.80	0.80	0.84	0.82
	64 × 64	0.82	0.81	0.81	0.81	0.81
	128 × 128	0.83	0.79	0.78	0.84	0.80
Nightset	32 × 32	0.76	0.50	0.72	0.55	0.66
	64 × 64	0.77	0.50	0.72	0.56	0.66
	128 × 128	0.76	0.48	0.68	0.58	0.65
(b) Part-set model
Roadset1	32 × 32	0.79	0.65	0.73	0.72	0.72
	64 × 64	0.81	0.66	0.73	0.75	0.74
	128 × 128	0.80	0.64	0.71	0.73	0.72
Roadset2	32 × 32	0.82	0.81	0.81	0.82	0.81
	64 × 64	0.83	0.81	0.82	0.82	0.82
	128 × 128	0.82	0.80	0.80	0.82	0.81
Nightset	32 × 32	0.76	0.51	0.74	0.54	0.67
	64 × 64	0.78	0.52	0.73	0.58	0.68
	128 × 128	0.76	0.49	0.71	0.55	0.65