Method

Adaptive Information Perception Paradigm for Online Multi-Object Tracking [AIPT]


Submitted on 25 Jun. 2023 06:57 by
Yukuan Zhang (Yunnan Normal University)

Running time:0.5 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
This study proposes a multi target tracking method
based on adaptive information perception paradigm,
which can improve the performance of the tracker.
Parameters:
bluure = 0.02
svt = 5
fit = 30
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 70.98 % 72.91 % 69.54 % 75.40 % 86.59 % 71.51 % 89.79 % 86.70 %
PEDESTRIAN 47.45 % 43.20 % 52.33 % 45.59 % 74.71 % 56.02 % 75.75 % 79.42 %

Benchmark TP FP FN
CAR 29768 4624 180
PEDESTRIAN 13535 9615 590

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 85.69 % 85.30 % 86.03 % 118 72.96 %
PEDESTRIAN 55.43 % 75.69 % 55.92 % 112 41.22 %

Benchmark MT rate PT rate ML rate FRAG
CAR 66.61 % 26.77 % 6.62 % 396
PEDESTRIAN 24.40 % 43.30 % 32.30 % 638

Benchmark # Dets # Tracks
CAR 29948 712
PEDESTRIAN 14125 263

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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