Method

Point3DT [la] [Point3DT]


Submitted on 15 Feb. 2020 06:36 by
Wang Sukai (The Hong Kong University of Science and Technology)

Running time:0.05 s
Environment:1 core @ >3.5 Ghz (Python)

Method Description:
We propose PointTrackNet, an end-to-end 3-D
object detection and tracking network, to
generate foreground masks, 3-D bounding boxes,
and point-wise tracking association displacements
for each detected object. The network merely
takes as input two adjacent point-cloud frames.
Experimental results show competitive results in
the irregularly and rapidly changing scenarios.
Parameters:
Detailed in the paper.
Latex Bibtex:
@inproceedings{PointTrackNet,
title={PointTrackNet: An End-to-End Network for
3-D Object Detection and Tracking from Point
Clouds},
author={Sukai Wang, Yuxiang Sun, Chengju Liu, and
Ming Liu},
booktitle = {to be submitted ICRA'20}
}

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 57.20 % 55.71 % 59.15 % 64.66 % 68.67 % 63.20 % 78.30 % 80.07 %

Benchmark TP FP FN
CAR 27955 6437 4424

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 67.56 % 76.83 % 68.42 % 294 48.73 %

Benchmark MT rate PT rate ML rate FRAG
CAR 60.46 % 26.77 % 12.77 % 756

Benchmark # Dets # Tracks
CAR 32379 1086

This table as LaTeX