GNNMatch: Graph Convolutional Neural Networks for Multi-Object Tracking via Sinkhorn Normalization

MOT16-01


Benchmark:

Short name:

GNNMatch

Detector:

Public

Description:

Reference:

I. Papakis, A. Sarkar, A. Karpatne. GCNNMatch: Graph Convolutional Neural Networks for Multi-Object Tracking via Sinkhorn Normalization. In arXiv preprint arXiv:2010.00067, 2020.

Last submitted:

March 02, 2021 (4 months ago)

Published:

July 30, 2020 at 21:33:18 CET

Submissions:

3

Open source:

Yes

Hardware:

TITAN RTX

Runtime:

0.3 Hz

Benchmark performance:

Sequence MOTA IDF1 HOTA MT ML FP FN Rcll Prcn AssA DetA AssRe AssPr DetRe DetPr LocA FAF ID Sw. Frag
MOT1657.255.044.6174 (22.9)258 (34.0)3,90573,49359.796.543.745.851.068.148.278.081.70.7559 (9.4)847 (14.2)

Detailed performance:

Sequence MOTA IDF1 HOTA MT ML FP FN Rcll Prcn AssA DetA AssRe AssPr DetRe DetPr LocA FAF ID Sw. Frag
MOT16-0144.041.736.0611903,47445.797.037.434.844.464.636.276.980.70.21928
MOT16-0368.463.750.661192,09030,87270.597.247.953.753.472.956.778.381.61.4117230
MOT16-0654.733.432.060805524,56460.492.722.246.355.529.649.776.282.40.5107130
MOT16-0746.746.737.19142588,36348.896.937.337.342.361.539.077.481.30.571140
MOT16-0835.337.732.992424110,54037.096.335.730.540.970.531.581.884.50.45067
MOT16-1248.555.645.717391204,13450.297.252.140.358.772.242.081.484.00.12030
MOT16-1433.638.829.6127155411,54637.592.631.428.136.959.129.572.979.70.7175222

Raw data:


GNNMatch