Method

sarlab_mot_yolo [SMY]
[Anonymous Submission]

Submitted on 26 Apr. 2019 23:30 by
[Anonymous Submission]

Running time:0.2 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
TBA
Parameters:
Yolov3 detector
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 59.32 % 75.34 % 60.64 % 79.82 %
PEDESTRIAN 37.73 % 70.38 % 39.29 % 90.69 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 67.45 % 95.92 % 79.21 % 25784 1096 12440 9.85 % 35053 4281
PEDESTRIAN 59.20 % 75.34 % 66.30 % 13828 4526 9529 40.69 % 24070 2681

Benchmark MT PT ML IDS FRAG
CAR 46.77 % 42.77 % 10.46 % 455 1302
PEDESTRIAN 25.09 % 58.76 % 16.15 % 361 1429

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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