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 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 73.02 % 75.62 % 71.20 % 80.10 % 84.56 % 73.84 % 89.00 % 86.52 %
PEDESTRIAN 40.35 % 44.48 % 36.93 % 49.91 % 66.83 % 41.05 % 70.19 % 77.81 %

Benchmark TP FP FN
CAR 31689 2703 886
PEDESTRIAN 15089 8061 2201

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 88.83 % 84.97 % 89.56 % 254 74.98 %
PEDESTRIAN 53.84 % 73.72 % 55.67 % 425 36.71 %

Benchmark MT rate PT rate ML rate FRAG
CAR 82.15 % 15.38 % 2.46 % 227
PEDESTRIAN 35.40 % 43.30 % 21.31 % 618

Benchmark # Dets # Tracks
CAR 32575 962
PEDESTRIAN 17290 655

This table as LaTeX