Method

Multi-Modal Late Fusion [MLF_1]
[Anonymous Submission]

Submitted on 11 Sep. 2019 20:40 by
[Anonymous Submission]

Running time:0.05 s
Environment:GPU @ 2.0 Ghz (Python)

Method Description:
A fast fusion network to easily upgrade performance
of single-modality detectors.
Parameters:
TBD
Latex Bibtex:
TBD

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) 94.34 % 91.59 % 79.14 %
Car (Orientation) 94.06 % 91.02 % 78.56 %
Car (3D Detection) 86.21 % 77.62 % 67.68 %
Car (Bird's Eye View) 90.44 % 87.28 % 75.09 %
Pedestrian (Detection) 67.93 % 52.32 % 47.77 %
Pedestrian (Orientation) 37.71 % 29.74 % 27.25 %
Pedestrian (3D Detection) 47.69 % 36.25 % 32.18 %
Pedestrian (Bird's Eye View) 53.09 % 41.32 % 37.16 %
Cyclist (Detection) 83.91 % 63.34 % 53.78 %
Cyclist (Orientation) 83.73 % 63.08 % 53.51 %
Cyclist (3D Detection) 77.86 % 55.58 % 48.49 %
Cyclist (Bird's Eye View) 81.02 % 56.42 % 49.28 %
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.
This figure as: png eps pdf txt gnuplot




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