Method

Quasi-Dense [on] [Quasi-Dense]
https://github.com/sysmm/quasi-dense

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:
@article{pang2020quasidense,
title={Quasi-Dense Instance Similarity Learning},
author={Pang, Jiangmiao and Qiu, Linlu and Chen,
Haofeng and Li, Qi and Darrell, Trevor and Yu,
Fisher},
journal={arXiv:2006.06664},
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 85.76 % 85.01 % 86.03 % 88.06 %
PEDESTRIAN 56.81 % 73.99 % 57.91 % 91.75 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 89.02 % 98.54 % 93.54 % 34782 517 4288 4.65 % 38708 1360
PEDESTRIAN 63.82 % 92.08 % 75.39 % 14925 1284 8460 11.54 % 18443 856

Benchmark MT PT ML IDS FRAG
CAR 69.08 % 27.85 % 3.08 % 93 617
PEDESTRIAN 31.27 % 49.83 % 18.90 % 254 1121

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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