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 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 24.60 % 73.89 % 24.66 % 86.68 %
PEDESTRIAN -10.16 % 59.10 % -9.68 % 93.86 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 33.73 % 86.67 % 48.57 % 12233 1881 24030 16.91 % 14603 598
PEDESTRIAN 12.94 % 37.56 % 19.25 % 3026 5031 20360 45.23 % 8308 360

Benchmark MT PT ML IDS FRAG
CAR 13.23 % 37.69 % 49.08 % 21 558
PEDESTRIAN 0.34 % 17.18 % 82.47 % 112 630

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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