Method

Aggregate View Object Detection with Feature Pyramid Network [la] [AVOD-FPN]
https://github.com/kujason/avod

Submitted on 11 Apr. 2018 18:07 by
Jason Ku (University of Waterloo)

Running time:0.1 s
Environment:Titan X (Pascal)

Method Description:
In the paper
Parameters:
In the paper
Latex Bibtex:
@article{ku2017joint,
title={Joint 3D Proposal Generation and Object Detection from View Aggregation},
author={Ku, Jason and Mozifian, Melissa and Lee, Jungwook and Harakeh, Ali and Waslander, Steven},
journal={arXiv preprint arXiv:1712.02294},
year={2017}
}

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
Car (Detection) 89.99 % 87.44 % 80.05 %
Car (Orientation) 89.95 % 87.13 % 79.74 %
Car (3D Detection) 81.94 % 71.88 % 66.38 %
Car (Bird's Eye View) 88.53 % 83.79 % 77.90 %
Pedestrian (Detection) 67.32 % 58.42 % 57.44 %
Pedestrian (Orientation) 53.36 % 44.92 % 43.77 %
Pedestrian (3D Detection) 50.80 % 42.81 % 40.88 %
Pedestrian (Bird's Eye View) 58.75 % 51.05 % 47.54 %
Cyclist (Detection) 68.65 % 59.32 % 55.82 %
Cyclist (Orientation) 67.61 % 57.53 % 54.16 %
Cyclist (3D Detection) 64.00 % 52.18 % 46.61 %
Cyclist (Bird's Eye View) 68.09 % 57.48 % 50.77 %
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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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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