Method

Adaptive Kalman Filtering and Hierarchical Data Association for 3D Multi-Object Tracking [AHMOT]
[Anonymous Submission]

Submitted on 8 Aug. 2024 11:26 by
[Anonymous Submission]

Running time:0.01 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
To address the challenge of three-dimensional
multi-object tracking (3D MOT) encountered by
autonomous vehicles in dynamic and complex traffic
environments, we propose an enhanced 3D MOT
framework. This framework integrates Adaptive
Kalman Filtering (AKF) with advanced diagnostic
and correction mechanisms to improve tracking
accuracy and system robustness. The AKF
dynamically adjusts filter parameters to adapt to
environmental changes, while the diagnostic and
correction mechanisms optimize the stability and
accuracy of state estimation. Furthermore, we
develop a novel data association model that
combines appearance, geometric, and distance
features of targets, significantly enhancing data
association accuracy.[la][on]
Parameters:
\
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
PEDESTRIAN 51.63 % 50.30 % 53.37 % 58.85 % 62.70 % 58.64 % 70.28 % 75.79 %

Benchmark TP FP FN
PEDESTRIAN 17964 5186 3762

Benchmark MOTA MOTP MODA IDSW sMOTA
PEDESTRIAN 59.96 % 71.12 % 61.35 % 321 37.55 %

Benchmark MT rate PT rate ML rate FRAG
PEDESTRIAN 52.23 % 37.46 % 10.31 % 1035

Benchmark # Dets # Tracks
PEDESTRIAN 21726 727

This table as LaTeX

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.


eXTReMe Tracker