Method

Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds [la] [on] [gp] [Complexer-YOLO]


Submitted on 16 Nov. 2018 13:43 by
Martin Simon (Valeo Schalter und Sensoren GmbH)

Running time:0.01 a
Environment:GPU @ 3.5 Ghz (C/C++)

Method Description:
Parameters:
Latex Bibtex:
@inproceedings{Simon_2019_CVPR_Workshops,
author = {Simon, Martin and Amende, Karl and
Kraus, Andrea and Honer, Jens and Samann, Timo
and Kaulbersch, Hauke and Milz, Stefan and
Michael Gross, Horst},
title = {Complexer-YOLO: Real-Time 3D Object
Detection and Tracking on Semantic Point
Clouds},
booktitle = {The IEEE Conference on Computer
Vision and Pattern Recognition (CVPR)
Workshops},
month = {June},
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 49.12 % 62.44 % 39.34 % 67.58 % 76.86 % 40.72 % 85.23 % 81.47 %
PEDESTRIAN 14.08 % 24.91 % 8.15 % 27.21 % 52.62 % 8.63 % 59.39 % 68.64 %

Benchmark TP FP FN
CAR 28583 5809 1658
PEDESTRIAN 8150 15000 3820

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 72.61 % 78.49 % 78.29 % 1952 54.73 %
PEDESTRIAN 11.99 % 62.31 % 18.70 % 1555 -1.28 %

Benchmark MT rate PT rate ML rate FRAG
CAR 56.92 % 37.23 % 5.85 % 1103
PEDESTRIAN 2.41 % 59.11 % 38.49 % 1649

Benchmark # Dets # Tracks
CAR 30241 2450
PEDESTRIAN 11970 1678

This table as LaTeX