MOT17Det Results

SELECT * FROM `DET_results` INNER JOIN `DET` ON `DET`.`id` = `DET_results`.`id_DET` INNER JOIN `DET_challenge` ON `DET_challenge`.`id_DET` = `DET`.`id` WHERE `DET_results`.`chl` = `DET_challenge`.`challenge` AND `DET_results`.`sub`=`DET_challenge`.`nsub` AND `DET_results`.`chl` ='9' AND `DET_challenge`.public = '1' AND `DET`.blocked=0 AND `DET_results`.`sequence`='OVERALL' ORDER BY AP DESC

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Tracker	AP	MODA	MODP	FAF	TP	FP	FN	Recall	Precision	F1	Hz
AInnoDetV2 1.	0.89 ±0.05	-58.4 ±126.7	79.1	29.5	107,733	174,608	6,831	94.0	38.2	54.3	296.0
	AInnovation: PC Attention Net
UNV_Det 2.	0.89 ±0.05	81.9 ±16.2	79.0	2.1	106,478	12,704	8,086	92.9	89.3	91.1	12.8
	Anonymous submission
PA_Det_NJ 3.	0.89 ±0.06	82.1 ±20.1	79.3	2.4	108,480	14,417	6,084	94.7	88.3	91.4	59.2
	PA_TECH_NJ
YTLAB 4.	0.89 ±0.07	76.7 ±13.1	80.2	2.8	104,555	16,685	10,009	91.3	86.2	88.7	22.3
	Z. Cai, Q. Fan, R. Feris, N. Vasconcelos. A unified multi-scale deep convolutional neural network for fast object detection. In European Conference on Computer Vision, 2016.
KDNT 5.	0.89 ±0.07	67.1 ±22.4	80.1	4.8	105,473	28,623	9,091	92.1	78.7	84.8	0.8
	F. Yu, W. Li, Q. Li, Y. Liu, X. Shi, J. Yan. POI: Multiple Object Tracking with High Performance Detection and Appearance Feature. In BMTT, SenseTime Group Limited, 2016.
F_ViPeD_B 6.	0.89 ±0.06	-14.4 ±115.1	77.4	20.8	106,698	123,194	7,831	93.2	46.4	62.0	14.8
	L. Ciampi, N. Messina, F. Falchi, C. Gennaro, G. Amato. Virtual to Real adaptation of Pedestrian Detectors for Smart Cities. In arXiv preprint arXiv:2001.03032, 2020.
ISE_Detv2 7.	0.88 ±0.05	67.4 ±12.8	79.9	4.9	106,094	28,865	8,470	92.6	78.6	85.0	3.2
	MIFD
ZIZOM 8.	0.81 ±0.05	72.0 ±22.0	79.8	2.2	95,414	12,990	19,139	83.3	88.0	85.6	2.4
	C. Lin, L. Jiwen, G. Wang, J. Zhou. Graininess-Aware Deep Feature Learning for Pedestrian Detection. In ECCV, 2018.
SDP 9.	0.81 ±0.12	76.9 ±16.2	78.0	1.3	95,699	7,599	18,865	83.5	92.6	87.9	0.6
	F. Yang, W. Choi, Y. Lin. Exploit All the Layers: Fast and Accurate CNN Object Detector With Scale Dependent Pooling and Cascaded Rejection Classifiers. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
EDet 10.	0.80 ±0.11	70.6 ±23.8	77.7	3.4	101,233	20,341	13,331	88.4	83.3	85.7	197.3
Tracker	AP	MODA	MODP	FAF	TP	FP	FN	Recall	Precision	F1	Hz
FRCNN 11.	0.72 ±0.13	68.5 ±0.0	78.0	1.7	88,601	10,081	25,963	77.3	89.8	83.1	5.1
	S. Ren, K. He, R. Girshick, J. Sun. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. In NIPS, 2015.
DPM 12.	0.61 ±0.14	31.2 ±10.8	75.8	7.1	78,007	42,308	36,557	68.1	64.8	66.4	19.7
	P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan. Object Detection with Discriminatively Trained Part Based Models. In TPAMI, 2010.
MHD 13.	0.49 ±0.20	11.2 ±42.9	69.9	8.8	64,637	51,801	49,927	56.4	55.5	56.0	3.0
	Mobilenet-based Human Detection
YLHDv2 14.	0.46 ±0.11	56.9 ±12.5	73.2	2.5	80,093	14,938	34,471	69.9	84.3	76.4	11.8
	https://arxiv.org/abs/1612.08242
HDGP 15.	0.45 ±0.20	42.1 ±20.1	76.4	1.3	55,680	7,436	58,884	48.6	88.2	62.7	0.6
	A. Garcia-Martin, R. Sanchez-Matilla, J. Martinez. Hierarchical detection of persons in groups. In Signal, Image and Video Processing, 2017.
VDet 16.	0.44 ±0.19	44.7 ±19.3	75.7	1.0	56,980	5,765	57,584	49.7	90.8	64.3	5.9
	Vitrociset Detection Algorithm
ACF 17.	0.32 ±0.00	18.1 ±0.0	72.1	2.8	37,312	16,539	77,252	32.6	69.3	44.3	74.0
	P. Dollar, R. Appel, S. Belongie, P. Perona. Fast Feature Pyramids for Object Detection. In TPAMI, 2014.

Sequences	Frames	Trajectories	Boxes
7	5919	785	188076