Method

Semi-Supervised Active Learning for Monocular Object Detection [MonoLiG]
[Anonymous Submission]

Submitted on 1 Mar. 2023 16:24 by
[Anonymous Submission]

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

Method Description:
We train MonoFlex with additional labels from the

Kitti-depth dataset using LiDAR predictions as

ground-truth information similar. We filter LiDAR labels using aleatoric

uncertainty after showing theoratically that it

imposes additional errors from the LiDAR modality.
Parameters:
N/A
Latex Bibtex:

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) 96.70 % 93.56 % 83.74 %
Car (Orientation) 96.56 % 93.23 % 83.42 %
Car (3D Detection) 24.90 % 18.86 % 16.79 %
Car (Bird's Eye View) 35.73 % 26.83 % 24.24 %
Pedestrian (Detection) 62.87 % 47.69 % 43.27 %
Pedestrian (Orientation) 52.66 % 38.92 % 35.05 %
Pedestrian (3D Detection) 2.89 % 1.94 % 1.91 %
Pedestrian (Bird's Eye View) 3.74 % 2.72 % 2.55 %
Cyclist (Detection) 80.41 % 58.35 % 51.21 %
Cyclist (Orientation) 76.10 % 54.91 % 47.58 %
Cyclist (3D Detection) 8.14 % 5.24 % 4.45 %
Cyclist (Bird's Eye View) 9.48 % 6.49 % 5.46 %
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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