Method

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

Submitted on 26 May. 2020 13:32 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. To adjust the method for MOTS, mask information is used for more precise fusion.
Parameters:
Using segmentation masks from MOTSFusion
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 74.66 % 76.11 % 73.75 % 79.59 % 90.24 % 76.27 % 92.70 % 90.46 %
PEDESTRIAN 57.65 % 60.30 % 56.19 % 63.45 % 81.58 % 60.19 % 83.35 % 83.65 %

Benchmark TP FP FN
CAR 31793 4967 628
PEDESTRIAN 15641 5056 458

Benchmark MOTSA MOTSP MODSA IDSW sMOTSA
CAR 83.53 % 89.59 % 84.78 % 458 74.53 %
PEDESTRIAN 72.05 % 81.51 % 73.36 % 270 58.08 %

Benchmark MT rate PT rate ML rate FRAG
CAR 67.12 % 29.43 % 3.45 % 655
PEDESTRIAN 43.33 % 42.96 % 13.70 % 664

Benchmark # Dets # Tracks
CAR 32421 959
PEDESTRIAN 16099 424

This table as LaTeX