Method

PML-BEV [la] [on] [PML-BEV]
[Anonymous Submission]

Submitted on 31 Jul. 2026 04:51 by
[Anonymous Submission]

Running time:0.02 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
Persistent Multi-Layer Bird's-Eye-View (PML-BEV)
LiDAR multi-object tracking. Uses K=4 height-
stratified elevation bands with 6-DOF ego-motion
compensation and ByteTrack tracking.
Parameters:
K=4 height slices, grid_res=0.10 m/px,
score_thresh=0.25, track_thresh=0.60
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 33.07 % 22.04 % 50.45 % 23.45 % 65.87 % 53.70 % 78.16 % 77.62 %
PEDESTRIAN 18.31 % 14.22 % 26.34 % 14.99 % 44.38 % 29.57 % 52.77 % 64.29 %

Benchmark TP FP FN
CAR 10361 24031 1882
PEDESTRIAN 2777 20373 5044

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 24.31 % 73.57 % 24.65 % 117 16.35 %
PEDESTRIAN -11.05 % 57.98 % -9.79 % 292 -16.09 %

Benchmark MT rate PT rate ML rate FRAG
CAR 13.23 % 37.54 % 49.23 % 650
PEDESTRIAN 0.34 % 17.53 % 82.13 % 862

Benchmark # Dets # Tracks
CAR 12243 554
PEDESTRIAN 7821 345

This table as LaTeX

This figure as: png pdf

This figure as: png pdf

[1] J. Luiten, A. Os̆ep, P. Dendorfer, P. Torr, A. Geiger, L. Leal-Taixé, B. Leibe: HOTA: A Higher Order Metric for Evaluating Multi-object Tracking. IJCV 2020.
[2] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[3] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


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