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 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 90.67 % 85.46 % 91.99 % 88.09 %
PEDESTRIAN 64.41 % 75.24 % 66.98 % 92.02 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 94.25 % 98.61 % 96.38 % 36702 517 2239 4.65 % 47313 2595
PEDESTRIAN 75.27 % 90.47 % 82.17 % 17613 1855 5788 16.68 % 23795 1561

Benchmark MT PT ML IDS FRAG
CAR 86.46 % 11.23 % 2.31 % 454 659
PEDESTRIAN 48.80 % 37.80 % 13.40 % 596 1195

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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