Review of Computer Vision Based Object Counting Methods

Fig. 1. Schematic diagrams of three models. (a) Regression based object counting model; (b) density estimation based object counting model; (c) multi-task model

Download full size

Fig. 2. Architecture of multi-scene judgment

Download full size

Fig. 3. Architecture of FCN-rLSTM

Download full size

Fig. 4. Input image and generation of density map. (a) Input image; (b) generation of density map

Download full size

Fig. 5. Architecture of Hydra CNN

Download full size

Fig. 6. Architecture of MCNN

Download full size

Fig. 7. Architecture of DecideNet

Download full size

Fig. 8. Structure of network of combined loss function

Download full size

Fig. 9. Architecture of SaCNN

Download full size

Fig. 10. Architecture of SFANet

Download full size

Fig. 11. Architecture of CAT-CNN

Download full size

Fig. 12. Architecture of FCN-MT

Download full size

Fig. 13. Architecture of cell segmentation network

Download full size

Fig. 14. Samples from six crowd datasets. (a) UCSD; (b) Mall; (c) UCF_CC_50; (d) WorldExpo’10; (e) Shanghai Tech Part A; (f) Shanghai Tech Part B

Download full size

Fig. 15. Samples from three cell datasets. (a) VGG Cells; (b) MBM Cells; (c) Adipocyte Cells

Download full size

Fig. 16. Samples from two datasets. (a) WebCamT; (b) TRANCOS

Download full size

Fig. 17. Estimation results on Shanghai Tech dataset generated by SFANet. The first two rows belong to Part B, and the last two rows belong to Part A^[58]. (a) Input images; (b) attention maps; (c) density maps; (d) ground truths

Download full size

Table 1. Summary of five public pedestrian datasets

View table

Table 1. Summary of five public pedestrian datasets

Dataset		Scene	Resolution	Range	Total number of people	Image No.
UCSD^[65]		Same	158×238	11-46	49885	2000
Mall^[66]		Same	240×320	13-53	62325	2000
UCF_CC_50^[67]		Different	Different	99-4543	63974	50
WorldExpo’10^[13]		Different	576×720	1-253	199923	3980
Shanghai Tech^[19]	Part A	Different	Different	33-3139	241677	482
Shanghai Tech^[19]	Part B	Different	768×1024	9-578	88488	716

Table 2. Summary of three public cell datasets
View table
Table 2. Summary of three public cell datasets
Dataset Resolution Range Total number of cells Image No.
VGG Cells^[17] 256×256 74—317 35192 200
MBM Cells^[40] 600×600 65—193 5446 44
Adipocyte Cells^[69] 150×150 48—299 31017 200

Table 3. Comparison of crowd counting models

View table

Table 3. Comparison of crowd counting models

Number	Method	UCSD^[65]		Mall^[66]		UCF_CC_50^[67]		WorldExpo’10^[13]		SHT A^[19]		SHT B^[19]
Number	Method	MAE	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE
1	Shang et al.^[6]					270.3		11.7
2	CNN boosting^[8]	1.10		2.01		364.4
3	Marsden et al.^[9]									85.7	131.1	17.7		28.6
4	Lempitsky et al.^[17]					493.4	487.1
5	Fiaschi et al.^[21]
6	MCNN^[19]	1.07	1.35			377.6	509.1	11.6		110.2	173.2	26.4		41.3
7	Hydra CNN^[11]					333.7	425.3
8	Wang et al.^[25]					264.9	382.1	8.6		83.7	124.5	17.9		32.4
9	FCN^[29]					338.6	424.5			126.5	173.5	23.8		33.1
10	A-CCNN^[30]	1.35				367.3
11	POCNet^[34]	1.24	1.50	1.82	5.48							12.1		20.3
12	DecideNet^[35]			1.52	1.90			9.23				20.8		29.4
13	SPN^[36]	1.03	1.32			259.2	335.9			61.7	99.5	9.4		14.4
14	AM-CNN^[43]					279.5	377.8	7.84		87.3	132.7	15.6		26.4
15	SCAR^[44]					259.0	374.0			66.3	114.1	9.5		15.2
16	Hossain et al.^[46]			1.28	1.68	271.6	391.0					16.9		28.4
17	RANet^[47]					239.8	319.4			59.4	102.0	7.9		12.9
18	ASNet^[48]					174.8	251.6	6.6		57.8	90.1
19	Wang et al.^[49]					170.1	232.4	6.5		57.7	99.7	7.4		11.1
20	Cross-scene^[13]	1.60	3.31			467.0	498.5	10.7		181.8	277.7	32.0		49.8
21	FF-CNN^[51]									81.8	138.8	16.5		26.2
22	MMCNN^[52]	1.02	1.18	1.98	5.68	320.6	323.8	9.1		91.2	128.6	18.5		29.3
23	DensityCNN^[53]					244.6	341.8	6.9		63.1	106.3	9.1		16.3
24	SaCNN^[55]					314.9	424.8	8.5		86.8	139.2	16.2		25.8
25	Sang et al.^[56]									75.8	124.9	11.0		18.6
26	MRA-CNN^[57]					240.8	352.6	7.5		74.2	112.5	11.9		21.3
27	SFANet^[58]	0.82	1.07			219.6	316.2			59.8	99.3	6.9		10.9
28	ACCNet^[59]	1.00	1.27			201.6	282.1			64.3	104.1	8.7		13.6
29	CAT-CNN^[60]					235.5	324.8	7.2		66.7	101.7	11.2		20.0
30	MSMT-CNN^[61]					319.5	358.1	9.3
31	GMN^[62]									95.8	133.3

Table 4. Comparison of cell counting models

View table

Table 4. Comparison of cell counting models

Number	Method	VGG Cells^[17]		MBM Cells^[40]		Adipocyte Cells^[69]
Number	Method	N=32	N=50	N=10	N=15	N=25	N=50
1	Marsden et al.^[9]			21.5±4.2	20.5±3.5
2	Lempitsky et al.^[17]	3.5±0.2
3	Fiaschi et al.^[21]	3.2±0.1
4	FCRN-A^[18]	2.9±0.2	2.9±0.2	22.2±11.6	21.3±9.4
5	Count-ception^[40]	2.4±0.4	2.3±0.4	10.7±2.5	8.8±2.3	21.9±2.8	19.4±2.2
6	Cell-Net^[42]		2.2±0.5	9.8±3.2
7	SAU-Net^[45]		2.6±0.4		5.7±1.2		14.2±1.6
8	GMN^[62]	3.6±0.3

Table 5. Comparison of vehicle counting models

View table

Table 5. Comparison of vehicle counting models

Number	Method	WebCamT^[12]		TRANCOS^[11]
Number	Method	Downtown	Parkway	GAME 0	GAME 1	GAME 2	GAME 3
1	Lempitsky et al.^[17]	5.91	5.19	13.76	16.72	20.72	24.36
2	Fiaschi et al.^[21]			17.77	20.14	23.65	25.99
3	Marsden et al.^[9]			9.70
4	FCN-rLSTM^[10]	1.53	1.63	4.38
5	CCNN^[11]			12.49	16.58	20.02	22.41
6	Hydra-CNN^[11]	3.55	3.64	10.99	13.75	16.69	19.32
7	AMDCN^[24]			9.77	13.16	15.00	15.87
8	CSRNet^[27]			3.56	5.49	8.57	15.04
9	DensityCNN^[53]			3.17	4.78	6.30	8.26
10	FCN-MT^[12]	2.74	2.52	5.31

Tools

Get Citation

Copy Citation Text

Ni Jiang, Haiyang Zhou, Feihong Yu. Review of Computer Vision Based Object Counting Methods[J]. Laser & Optoelectronics Progress, 2021, 58(14): 1400002

Download Citation

EndNote(RIS)BibTex Plain Text

Set citation alerts for article

Save article for my favorites