Method

TuSimple [on] [TuSimple]


Submitted on 18 Sep. 2016 07:28 by
Jian Guo (Beihang University & TuSimple)

Running time:0.6 s
Environment:1 core @ 2.5 Ghz (Matlab + C/C++)

Method Description:
Parameters:
Latex Bibtex:
@inproceedings{choi2015near,
title={Near-online multi-target tracking with aggregated local flow
descriptor},
author={Choi, Wongun},
booktitle={Proceedings of the IEEE International Conference on
Computer Vision},
pages={3029--3037},
year={2015}
}
@inproceedings{he2016deep,
title={Deep residual learning for image recognition},
author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and
Sun, Jian},
booktitle={Proceedings of the IEEE conference on computer vision
and pattern recognition},
pages={770--778},
year={2016}
}

Detailed Results

From all 29 test sequences, our benchmark computes the HOTA tracking metrics (HOTA, DetA, AssA, DetRe, DetPr, AssRe, AssPr, LocA) [1] as well as the CLEARMOT, MT/PT/ML, identity switches, and fragmentation [2,3] metrics. The tables below show all of these metrics.


Benchmark HOTA DetA AssA DetRe DetPr AssRe AssPr LocA
CAR 71.55 % 72.62 % 71.11 % 76.78 % 83.84 % 74.51 % 86.26 % 85.72 %
PEDESTRIAN 45.88 % 44.66 % 47.62 % 47.92 % 69.51 % 52.04 % 69.88 % 76.43 %

Benchmark TP FP FN
CAR 30736 3656 759
PEDESTRIAN 14772 8378 1189

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 86.31 % 84.05 % 87.16 % 292 72.06 %
PEDESTRIAN 57.61 % 71.73 % 58.67 % 246 39.57 %

Benchmark MT rate PT rate ML rate FRAG
CAR 71.08 % 22.00 % 6.92 % 220
PEDESTRIAN 30.58 % 44.33 % 25.09 % 651

Benchmark # Dets # Tracks
CAR 31495 793
PEDESTRIAN 15961 336

This table as LaTeX