Method

Feature-Fused 3 Frame [FF3F]
[Anonymous Submission]

Submitted on 29 Jul. 2026 23:02 by
[Anonymous Submission]

Running time:.009 s
Environment:>8 cores @ >3.5 Ghz (Python)

Method Description:
FF3F-KITTI is an online modular multi-object
tracking system that processes RGB images and
Velodyne point clouds through separate specialized
branches. Confidence-aware fusion combines the
useful detection and geometric information, while
ego-motion compensation and a lightweight
AssociationNet connect detections across frames.
Class-specific routing enables or disables
branches according to their usefulness for Car and
Pedestrian tracking.
Parameters:
Car RGB detector: 4 epochs, AdamW optimizer,
initial learning rate = 0.0002, weight decay =
0.0005, batch size = 6, warm-up = 1 epoch, and
input resolution = 960 pixels. Each of the five
FusionNet heads was trained for 5 epochs using
AdamW, with learning rate = 0.001, weight decay =
0.0001, and batch size = 1024. AssociationNet was
trained for 10 epochs using AdamW, with learning
rate = 0.0005, weight decay = 0.0001, and matrix
batch size = 16.
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 71.44 % 70.70 % 72.69 % 78.65 % 80.06 % 75.45 % 89.13 % 86.53 %
PEDESTRIAN 34.67 % 40.12 % 30.41 % 50.00 % 55.30 % 32.79 % 69.44 % 75.08 %

Benchmark TP FP FN
CAR 31122 3270 2662
PEDESTRIAN 15179 7971 5749

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 81.97 % 84.89 % 82.75 % 270 68.30 %
PEDESTRIAN 36.39 % 70.80 % 40.73 % 1006 17.24 %

Benchmark MT rate PT rate ML rate FRAG
CAR 72.77 % 23.23 % 4.00 % 280
PEDESTRIAN 34.36 % 49.14 % 16.50 % 1050

Benchmark # Dets # Tracks
CAR 33784 1273
PEDESTRIAN 20928 1200

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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