Method

centernet_deepsort [CD]
[Anonymous Submission]

Submitted on 10 Jun. 2020 18:36 by
[Anonymous Submission]

Running time:20 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
centernet-detection;
deepsort-tracking;
dataset-market+veri+kitti
method-compare
Parameters:
centernet-detection;
deepsort-tracking
Latex Bibtex:
centernet-detection;
deepsort-tracking

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 79.62 % 83.48 % 80.15 % 87.15 %
PEDESTRIAN 53.31 % 73.67 % 55.11 % 92.08 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 85.02 % 96.68 % 90.47 % 32418 1113 5713 10.01 % 36272 1539
PEDESTRIAN 63.32 % 89.16 % 74.05 % 14828 1803 8590 16.21 % 18254 1082

Benchmark MT PT ML IDS FRAG
CAR 60.31 % 34.31 % 5.38 % 184 701
PEDESTRIAN 28.87 % 47.42 % 23.71 % 416 1169

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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