Method

CenterTrack[on] [at] [CenterTrack]
https://github.com/xingyizhou/CenterTrack

Submitted on 28 Oct. 2019 01:44 by
Xingyi Zhou (University of Texas at Austin)

Running time:0.045s
Environment:GPU

Method Description:
Our model takes the current frame, the previous
frame, and
a heatmap rendered from previous tracking results as
input,
and predicts the current detection heatmap as well
as their
offsets to centers in the previous frame.
Parameters:
See the code for details.
Latex Bibtex:
@article{zhou2020tracking,
title={Tracking Objects as Points},
author={Zhou, Xingyi and Koltun, Vladlen and
Krähenbühl, Philipp},
journal={ECCV},
year={2020}
}

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 89.44 % 85.05 % 89.78 % 87.81 %
PEDESTRIAN 55.34 % 74.02 % 55.75 % 92.01 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 93.20 % 97.73 % 95.41 % 36562 849 2666 7.63 % 45465 1351
PEDESTRIAN 65.61 % 87.48 % 74.98 % 15351 2196 8047 19.74 % 22588 806

Benchmark MT PT ML IDS FRAG
CAR 82.31 % 15.38 % 2.31 % 116 334
PEDESTRIAN 34.71 % 45.36 % 19.93 % 95 751

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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