RNN tracker with appearance model in my detection


Short name:

RNN_A_md

Benchmark:

Description:

n/a

Hardware:

Intel(R) Xeon(R) CPU E5-1603 v4@ 2.80GHz 2.80 GHz

Detector:

Private

Processing:

Online

Last submitted:

June 14, 2019 (4 months ago)

Published:

June 14, 2019 at 16:20:25 CET

Submissions:

1

Open source:

No

Project page / code:

n/a

Reference:

Anonymous submission

Benchmark performance:

MOTAMOTPFAFMTMLFPFNID Sw.FragSpecificationsDetector
32.172.02.412.8 % 38.6 % 14,295108,1211,4704,107Intel(R) Xeon(R) CPU E5-1603 v4@ 2.80GHz 2.80 GHzPrivate
IDF1ID PrecisionID Recall
35.955.026.7

Detailed performance:

Sequence MOTA IDF1 MOTP FAF GT MT ML FP FN ID Sw Frag
MOT16-0127.035.570.11.62313.0 % 43.5 % 7133,89361164
MOT16-0334.034.871.65.41488.1 % 31.1 % 8,07260,0867982,242
MOT16-0649.046.373.20.822121.7 % 28.1 % 9854,776120330
MOT16-0726.337.372.12.3549.3 % 46.3 % 1,12510,805103400
MOT16-0830.234.874.51.96315.9 % 31.7 % 1,17510,342159266
MOT16-1239.150.675.10.98616.3 % 44.2 % 7774,22252117
MOT16-1415.527.069.21.91643.0 % 56.1 % 1,44813,997177588

Raw data:

n/a


RNN_A_md