Method

TrackR-CNN [TrackR-CNN]
https://github.com/VisualComputingInstitute/TrackR-CNN

Submitted on 4 Dec. 2019 19:54 by
Paul Voigtlaender (RWTH Aachen University)

Running time:0.5 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
Mask R-CNN with ReID head and 3D convs.
http://openaccess.thecvf.com/content_CVPR_2019/htm
l/Voigtlaender_MOTS_Multi-
Object_Tracking_and_Segmentation_CVPR_2019_paper.h
tml
Parameters:
Default (tuned on KITTI MOTS train set)
Latex Bibtex:
@inproceedings{Voigtlaender19CVPR_MOTS,
author = {Paul Voigtlaender and Michael Krause
and Aljo\u{s}a O\u{s}ep and Jonathon Luiten
and Berin Balachandar Gnana Sekar and
Andreas Geiger and Bastian Leibe},
title = {{MOTS}: Multi-Object Tracking and
Segmentation},
booktitle = {CVPR},
year = {2019},
}

Detailed Results

From all 29 test sequences, our benchmark computes the HOTA tracking metrics (HOTA, DetA, AssA, DetRe, DetPr, AssRe, AssPr, LocA) [1] as well as the CLEARMOT, MT/PT/ML, identity switches, and fragmentation [2,3] metrics. The tables below show all of these metrics.


Benchmark HOTA DetA AssA DetRe DetPr AssRe AssPr LocA
CAR 56.63 % 69.90 % 46.53 % 74.63 % 84.18 % 63.13 % 62.33 % 86.60 %
PEDESTRIAN 41.93 % 53.75 % 33.84 % 57.85 % 72.51 % 45.30 % 50.74 % 78.03 %

Benchmark TP FP FN
CAR 31284 5476 1307
PEDESTRIAN 15342 5355 1171

Benchmark MOTSA MOTSP MODSA IDSW sMOTSA
CAR 79.67 % 85.08 % 81.55 % 692 66.97 %
PEDESTRIAN 66.14 % 74.60 % 68.47 % 482 47.31 %

Benchmark MT rate PT rate ML rate FRAG
CAR 74.92 % 22.82 % 2.25 % 1068
PEDESTRIAN 45.56 % 41.11 % 13.33 % 880

Benchmark # Dets # Tracks
CAR 32591 681
PEDESTRIAN 16513 316

This table as LaTeX