Method

IMOU_ALG [IMOU_ALG]


Submitted on 30 Dec. 2022 11:21 by
Zion Ma (Zhejiang University)

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

Method Description:
TBD
Parameters:
TBD
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 92.76 % 85.73 % 92.85 % 87.75 %
PEDESTRIAN 72.29 % 73.28 % 73.59 % 91.13 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 97.24 % 96.36 % 96.80 % 37150 1403 1055 12.61 % 47288 716
PEDESTRIAN 79.86 % 93.12 % 85.99 % 18756 1385 4729 12.45 % 24291 400

Benchmark MT PT ML IDS FRAG
CAR 90.31 % 4.31 % 5.38 % 31 72
PEDESTRIAN 52.58 % 37.11 % 10.31 % 301 926

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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