Method

Score Update Based Adaptive Multi-Object Tracking [st] [la] [SUAMOT]
[Anonymous Submission]

Submitted on 11 Jun. 2023 10:38 by
[Anonymous Submission]

Running time:0.01 s
Environment:8 cores @ >3.5 Ghz (Python + C/C++)

Method Description:
By integrating the detection results of lidar
based target detectors and camera based target
detectors, the false detection of target
trajectories is greatly reduced. Aiming at the
problem that the target tracking algorithm is
highly dependent on the results of the target
detector, this paper adds an adaptive factor based
on the confidence of the detection frame to the
algorithm, so that the algorithm can adaptively
adjust the Covariance matrix of the Kalman filter
in the tracking algorithm and the weight of each
cost function in the data association module
according to the confidence of the detection
frame.
Parameters:
N/A
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 73.78 % 69.71 % 78.52 % 72.27 % 86.89 % 80.74 % 90.11 % 86.91 %

Benchmark TP FP FN
CAR 28497 5895 109

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 82.36 % 85.28 % 82.54 % 63 70.16 %

Benchmark MT rate PT rate ML rate FRAG
CAR 57.08 % 33.38 % 9.54 % 730

Benchmark # Dets # Tracks
CAR 28606 630

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.


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