Method

Feature-Aware Tracking with Integrated Neural Networks [Fatinn]
[Anonymous Submission]

Submitted on 25 Mar. 2025 17:17 by
[Anonymous Submission]

Running time:1 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
Fatinn is a novel multi-object tracking (MOT)
framework designed to enhance both detection and
association accuracy. By leveraging feature-aware
mechanisms and integrated neural networks, Fatinn
effectively captures dynamic object motion patterns
and improves tracking robustness in complex
environments.
Parameters:
iou=0.65
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 77.97 % 77.81 % 78.80 % 81.58 % 85.88 % 81.14 % 90.27 % 86.88 %
PEDESTRIAN 51.65 % 52.29 % 51.35 % 57.90 % 70.78 % 55.04 % 75.97 % 79.01 %

Benchmark TP FP FN
CAR 32137 2255 533
PEDESTRIAN 17339 5811 1597

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 90.31 % 85.41 % 91.89 % 545 76.67 %
PEDESTRIAN 64.34 % 75.04 % 68.00 % 848 45.64 %

Benchmark MT rate PT rate ML rate FRAG
CAR 86.31 % 11.38 % 2.31 % 271
PEDESTRIAN 47.77 % 38.14 % 14.09 % 676

Benchmark # Dets # Tracks
CAR 32670 1292
PEDESTRIAN 18936 1085

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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