Method

EagerMOT: 3D Multi-Object Tracking via Sensor Fusion [EagerMOT]
https://github.com/aleksandrkim61/EagerMOT

Submitted on 31 Oct. 2020 11:27 by
Aleksandr Kim (Technical University of Munich)

Running time:0.011 s
Environment:4 cores @ 3.0 Ghz (Python)

Method Description:
A simple real-time 3D tracking pipeline built using standard components:

During each frame, independent detections from 3D and 2D are fused together into individual object instances. These instances are matched to existing tracks during two consecutive stages: first using 3D information (3D bounding box IoU) and then using 2D information (2D bounding box IoU).

The method is suitable for 3D Multi-Object Tracking and requires only bounding box level detections.
Parameters:
TBA
Latex Bibtex:
@inproceedings{Kim21ICRA,
title = {EagerMOT: 3D Multi-Object Tracking via Sensor Fusion},
author = {Kim, Aleksandr and Osep, Aljo\v{s}a and Leal-Taix{'e}, Laura},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
year = {2021}
}

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 74.39 % 75.27 % 74.16 % 78.77 % 86.42 % 76.24 % 91.05 % 87.17 %
PEDESTRIAN 39.38 % 40.60 % 38.72 % 43.43 % 61.49 % 40.98 % 68.33 % 71.25 %

Benchmark TP FP FN
CAR 30895 3497 454
PEDESTRIAN 14191 8959 2161

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 87.82 % 85.69 % 88.51 % 239 74.97 %
PEDESTRIAN 49.82 % 64.42 % 51.97 % 496 28.01 %

Benchmark MT rate PT rate ML rate FRAG
CAR 76.15 % 21.39 % 2.46 % 390
PEDESTRIAN 27.49 % 48.45 % 24.05 % 1410

Benchmark # Dets # Tracks
CAR 31349 922
PEDESTRIAN 16352 724

This table as LaTeX