Method

Quasi-Dense [on] [Quasi-Dense]
https://github.com/SysCV/qdtrack

Submitted on 21 May. 2020 21:07 by
Linlu Qiu (Georgia Institute of Technology)

Running time:0.07s
Environment:GPU (Python)

Method Description:
We present a simple yet effective quasi-dense
matching method to learn instance similarity from
hundreds of region proposals in a pair
of images. In the resulting feature space, a simple
nearest neighbor search can distinguish different
instances without bells and whistles. We apply our
method to joint object detection and tracking, along
with Faster R-CNN + ResNet50 without using location
or motion heuristics.
Parameters:
See the code for details.
Latex Bibtex:
@inproceedings{pang2021quasidense,
title={Quasi-Dense Similarity Learning for Multiple Object Tracking},
author={Pang, Jiangmiao and Qiu, Linlu and Li, Xia and Chen,
Haofeng and Li, Qi and Darrell, Trevor and Yu,
Fisher},
journal = {CVPR},
year = {2021}
}

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 68.45 % 72.44 % 65.49 % 76.01 % 85.37 % 68.28 % 88.53 % 86.50 %
PEDESTRIAN 41.12 % 44.81 % 38.10 % 48.55 % 70.39 % 41.02 % 72.47 % 77.87 %

Benchmark TP FP FN
CAR 30072 4320 549
PEDESTRIAN 14657 8493 1309

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 84.93 % 84.85 % 85.84 % 313 71.69 %
PEDESTRIAN 55.55 % 73.70 % 57.66 % 487 38.90 %

Benchmark MT rate PT rate ML rate FRAG
CAR 69.54 % 26.61 % 3.85 % 567
PEDESTRIAN 31.27 % 48.45 % 20.27 % 951

Benchmark # Dets # Tracks
CAR 30621 979
PEDESTRIAN 15966 702

This table as LaTeX