Method

3D-CNN/PMBM [on] [gp] [3D-CNN/PMBM]


Submitted on 27 Jan. 2018 01:51 by
Samuel Scheidegger (Chalmers University of Technology)

Running time:0.01 s
Environment:1 core @ 3.0 Ghz (Matlab)

Method Description:
Output from CNN detector processed by PMBM filter.
Parameters:
threshold=0.9
Latex Bibtex:
@inproceedings{Scheidegger2018,
author = {Samuel Scheidegger and
Joachim Benjaminsson and
Emil Rosenberg and
Amrit Krishnan and
Karl Granström},
title = {Mono-Camera 3D Multi-Object Tracking
Using Deep Learning Detections
and {PMBM} Filtering},
booktitle = {2018 {IEEE} Intelligent Vehicles
Symposium, {IV} 2018, Changshu, Suzhou,
China, June 26-30, 2018},
pages = {433--440},
year = {2018},
crossref = {DBLP:conf/ivs/2018},
url = {https://doi.org/10.1109/IVS.2018.8500454},
doi = {10.1109/IVS.2018.8500454},
timestamp = {Thu, 25 Oct 2018 18:12:58 +0200},
biburl =
{https://dblp.org/rec/bib/conf/ivs/ScheideggerBRKG18},
bibsource = {dblp computer science bibliography,
https://dblp.org}
}

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 59.12 % 65.43 % 54.28 % 69.87 % 80.68 % 57.28 % 83.89 % 83.94 %

Benchmark TP FP FN
CAR 28758 5634 1024

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 79.23 % 81.58 % 80.64 % 485 63.83 %

Benchmark MT rate PT rate ML rate FRAG
CAR 62.77 % 30.77 % 6.46 % 554

Benchmark # Dets # Tracks
CAR 29782 1225

This table as LaTeX