TrctrD15: deepMOTTracktor15

TUD-Crossing


Benchmark:

Short name:

TrctrD15

Detector:

Public

Description:

n/a

Project page / code:

n/a

Reference:

Y. Xu, A. Osep, Y. Ban, R. Horaud, L. Leal-Taixe, X. Alameda-Pineda. How To Train Your Deep Multi-Object Tracker. In , 2019.

Processing:

Online

Last submitted:

February 05, 2020 (4 months ago)

Published:

April 16, 2020 at 14:18:39 CET

Submissions:

3

Open source:

Yes

Hardware:

GTX-TITAN XP

Runtime:

1.6 Hz

Benchmark performance:

Sequence MOTA IDF1 MOTP MT ML FP FN Recall Precision FAF ID Sw. Frag
2D MOT 201544.146.075.3124 (17.2)192 (26.6)6,08526,91756.285.01.11,347 (24.0)1,868 (33.2)

Detailed performance:

Sequence MOTA IDF1 MOTP MT ML FP FN Recall Precision FAF ID Sw. Frag
ADL-Rundle-134.551.473.51032,3603,68760.470.44.753162
ADL-Rundle-345.544.881.3877434,72253.688.01.27693
AVG-TownCentre34.335.269.633447233,31853.684.11.6657668
ETH-Crossing43.958.483.12102053346.995.90.11019
ETH-Jelmoli57.566.379.4161230875270.485.30.71947
ETH-Linthescher49.155.680.430991584,33751.496.70.15193
KITTI-1649.747.573.51111369859.089.90.54479
KITTI-1949.558.371.59105392,08960.985.80.571138
PETS09-S2L247.129.371.5314804,31655.291.71.1307492
TUD-Crossing77.547.476.1801121480.698.80.12323
Venice-136.139.074.1456302,25150.778.61.43654

Raw data: