MOT Challenge

MOT20-04

MOT20-06

MOT20-07

MOT20-08

Benchmark:

MOT16 | MOT17 | MOT20 | MOT15 |

Short name:

CSTrack

Detector:

Private

Description:

CSTrack proposes a strong ReID based one-shot MOT framework. It includes a novel cross-correlation network that can effectively impel the separate branches to learn task-dependent representations, and a scale-aware attention network that learns discriminative embeddings to improve the ReID capability. This work also provides an analysis of the weak data association ability in one-shot MOT methods. Our improvements make the data association ability of our one-shot model is comparable to two-stage methods while running more faster.

Reference:

C. Liang, Z. Zhang, Y. Lu, X. Zhou, B. Li. Rethinking the competition between detection and ReID in Multi-Object Tracking. In arXiv:2010.12138 [cs], 2020.

Last submitted:

December 23, 2020 (3 years ago)

Published:

December 15, 2020 at 07:25:46 CET

Submissions:

Project page / code:

https://github.com/JudasDie/SOTS

Open source:

Yes

Hardware:

RTX2080ti

Runtime:

4.5 Hz

Benchmark performance:

Sequence	MOTA	IDF1	HOTA	MT	ML	FP	FN	Rcll	Prcn	AssA	DetA	AssRe	AssPr	DetRe	DetPr	LocA	FAF	ID Sw.	Frag
MOT20	66.6	68.6	54.0	626 (50.4)	192 (15.5)	25,404	144,358	72.1	93.6	54.0	54.2	57.6	77.8	58.5	76.0	81.5	5.7	3,196 (44.3)	7,632 (105.9)

Detailed performance:

Sequence	MOTA	IDF1	HOTA	MT	ML	FP	FN	Rcll	Prcn	AssA	DetA	AssRe	AssPr	DetRe	DetPr	LocA	FAF	ID Sw.	Frag
MOT20-04	80.2	79.0	61.2	439	32	12,899	40,226	85.3	94.8	59.3	63.4	62.9	79.0	68.6	76.2	81.2	6.2	1,021	3,061
MOT20-06	50.7	53.9	43.7	80	86	6,234	58,185	56.2	92.3	44.1	43.5	47.3	75.1	46.5	76.5	82.4	6.2	1,065	2,547
MOT20-07	73.5	66.4	55.0	73	3	2,475	5,910	82.1	91.7	50.0	61.3	54.2	76.1	67.9	75.8	82.4	4.2	398	523
MOT20-08	42.5	50.7	39.3	34	71	3,796	40,037	48.3	90.8	42.6	36.4	45.6	75.4	39.0	73.4	81.3	4.7	712	1,501

Raw data:

download

RTAT

CSTrack

Fair

CSTrack: Rethinking the competition between detection and ReID in Multi-Object Tracking