Method

HWFD [on] [HWFD]


Submitted on 26 Dec. 2019 08:49 by
zhang pengfei (fudan university)

Running time:0.03 s
Environment:one 1080Ti

Method Description:
Using Velocity Estimation for large distance.
The detection time is not included.
Parameters:
TBA
Latex Bibtex:
TBA

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
PEDESTRIAN 67.27 % 74.00 % 67.77 % 91.80 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
PEDESTRIAN 73.74 % 93.00 % 82.26 % 17296 1302 6159 11.70 % 21637 372

Benchmark MT PT ML IDS FRAG
PEDESTRIAN 44.67 % 32.99 % 22.34 % 116 918

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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