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 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 82.48 % 85.51 % 82.55 % 88.51 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 84.32 % 99.66 % 91.35 % 31679 107 5893 0.96 % 33944 736

Benchmark MT PT ML IDS FRAG
CAR 56.92 % 33.54 % 9.54 % 24 627

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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