Method

CenterTube-P [CenterTube-P]
[Anonymous Submission]

Submitted on 20 Oct. 2021 09:30 by
[Anonymous Submission]

Running time:0.09 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
3D Multi-Object Tracking (MOT) in dynamic point
cloud sequences is a crucial component for
numerous intelligent systems such as autonomous
driving and robotics.In this paper, we propose
a concise one-step model CenterTube by
formulating the problem of target trajectory
prediction as 4D tubelet detection in a short
video clip. Our CenterTube is inspired by the
anchor-free 3D detector CenterPoint and
consists of three head branches: (1) a Center
Branch to detect the center points of objects
and recognize their category in key frame; (2)
a Box Branch to regress 3D size and orientation
of bounding box; (3) a Movement Branch to
estimate instance movement and frame interval.
Further, a Tube BEV-IoU (TB-IoU) is proposed to
link the generated clip-level tubelets and form
the final tracks.
Parameters:
python=3.6
Latex Bibtex:

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 67.92 % 68.73 % 68.32 % 75.30 % 80.42 % 73.80 % 82.32 % 85.61 %

Benchmark TP FP FN
CAR 30214 4178 1989

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 80.30 % 83.73 % 82.07 % 607 66.01 %

Benchmark MT rate PT rate ML rate FRAG
CAR 70.31 % 20.15 % 9.54 % 591

Benchmark # Dets # Tracks
CAR 32203 712

This table as LaTeX