Method

Pillar Feature Network [PiFeNet]
https://github.com/ldtho/PiFeNet

Submitted on 19 Feb. 2022 06:04 by
Tho Le (Monash)

Running time:0.03 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
TBD
Parameters:
TBD
Latex Bibtex:
@article{le2022accurate,
title={Accurate and Real-time 3D Pedestrian
Detection Using an Efficient Attentive Pillar
Network},
author={Le, Duy Tho and Shi, Hengcan and
Rezatofighi, Hamid and Cai, Jianfei},
journal={IEEE Robotics and Automation Letters},
year={2022},
publisher={IEEE}
}

Detailed Results

Object detection and orientation estimation results. Results for object detection are given in terms of average precision (AP) and results for joint object detection and orientation estimation are provided in terms of average orientation similarity (AOS).


Benchmark Easy Moderate Hard
Pedestrian (Detection) 72.74 % 62.35 % 59.29 %
Pedestrian (Orientation) 55.11 % 46.59 % 44.14 %
Pedestrian (3D Detection) 56.39 % 46.71 % 42.71 %
Pedestrian (Bird's Eye View) 63.25 % 53.92 % 50.53 %
Cyclist (Detection) 78.05 % 63.34 % 56.46 %
Cyclist (Orientation) 77.54 % 62.62 % 55.66 %
Cyclist (3D Detection) 67.50 % 51.10 % 44.66 %
Cyclist (Bird's Eye View) 72.80 % 56.94 % 50.04 %
This table as LaTeX


2D object detection results.
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Orientation estimation results.
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3D object detection results.
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Bird's eye view results.
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2D object detection results.
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Orientation estimation results.
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3D object detection results.
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Bird's eye view results.
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