Method

DiTMOT [DiTMOT]
https://github.com/StevenWang30/DiTNet

Submitted on 13 Apr. 2021 07:34 by
Wang Sukai (The Hong Kong University of Science and Technology)

Running time:0.08 s
Environment:1 core @ >3.5 Ghz (Python)

Method Description:
End-to-end 3D object detection and tracking based
on point clouds is receiving more and more
attention in many robotics applications, such as
autonomous driving. Compared with 2D images, 3D
point clouds do not have enough texture
information for data association. Thus, we
propose an end-to- end point cloud-based network,
DiTNet, to directly assign a track ID to each
object across the whole sequence, without the
data association step. DiTNet is made location-
invariant by using relative location and
embeddings to learn each object’s spatial and
temporal features in the Spatio-temporal world.
The features from the detection module helps to
improve the tracking performance, and the
tracking module with final trajectories also
helps to refine the detection results.
Parameters:
Detailed in the paper.
Latex Bibtex:
@article{wang2021ditnet,
title={DiTNet: End-to-End 3D Object Detection
and Track ID Assignment in Spatio-Temporal
World},
author={Wang, Sukai and Cai, Peide and Wang,
Lujia and Liu, Ming},
journal={IEEE Robotics and Automation Letters},
volume={6},
number={2},
pages={3397--3404},
year={2021},
publisher={IEEE}
}

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 84.73 % 84.40 % 84.82 % 87.76 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 89.03 % 96.81 % 92.76 % 33427 1101 4118 9.90 % 39491 857

Benchmark MT PT ML IDS FRAG
CAR 74.92 % 12.15 % 12.92 % 31 188

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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