Method

CSFADet + track header [CSFADet-tracker]


Submitted on 30 Oct. 2019 09:45 by
Zhenjia Fan (Fuzhou University)

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

Method Description:
End-to-end object detection and tracking system
Parameters:
None
Latex Bibtex:
None

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 77.73 % 84.01 % 80.29 % 87.13 %
PEDESTRIAN 44.57 % 72.56 % 48.38 % 90.98 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 85.64 % 96.44 % 90.72 % 33141 1222 5555 10.99 % 39708 2133
PEDESTRIAN 67.80 % 78.29 % 72.66 % 15881 4405 7544 39.60 % 25579 2051

Benchmark MT PT ML IDS FRAG
CAR 65.54 % 30.46 % 4.00 % 883 1417
PEDESTRIAN 35.05 % 54.30 % 10.65 % 882 1803

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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