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 commonly used tracking metrics CLEARMOT, MT/PT/ML, identity switches, and fragmentations [1,2]. The tables below show all of these metrics.


Benchmark MOTA MOTP MODA MODP
CAR 88.21 % 85.73 % 88.56 % 88.57 %
PEDESTRIAN 51.11 % 64.75 % 52.13 % 89.25 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 90.60 % 98.69 % 94.47 % 33638 446 3489 4.01 % 37846 1206
PEDESTRIAN 61.70 % 87.02 % 72.20 % 14394 2147 8936 19.30 % 18380 944

Benchmark MT PT ML IDS FRAG
CAR 76.62 % 20.92 % 2.46 % 121 474
PEDESTRIAN 27.84 % 48.11 % 24.05 % 234 1378

This table as LaTeX

[1] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[2] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


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