Method

A Joint Multi-object Tracking Method with Center-based Feature Extraction and Occlusion Handling [CJMODT-v2]
[Anonymous Submission]

Submitted on 9 Nov. 2022 03:40 by
[Anonymous Submission]

Running time:0.05 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
This work introduces CJMODT, a center-based multi-
object
detection and tracking algorithm for MOT. It
includes a newly
proposed center-based feature extraction framework
for multi-
object detection.
Parameters:
λf= 0.5, λs= 0.1, and λo= 0.5
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 86.22 % 85.81 % 86.63 % 88.69 %
PEDESTRIAN 55.58 % 75.05 % 56.02 % 92.36 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 89.00 % 99.14 % 93.80 % 34763 302 4296 2.71 % 38152 1645
PEDESTRIAN 61.79 % 91.98 % 73.92 % 14432 1258 8923 11.31 % 17345 1087

Benchmark MT PT ML IDS FRAG
CAR 68.15 % 26.15 % 5.69 % 140 394
PEDESTRIAN 31.27 % 41.92 % 26.80 % 102 734

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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