Method

LMnet [la] [LMnet]
[Anonymous Submission]

Submitted on 29 Aug. 2017 15:36 by
[Anonymous Submission]

Running time:0.1 s
Environment:GPU @ 1.1 Ghz (Python + C/C++)

Method Description:
Based on FCN network. VGG16 network as feature
extractor.
Parameters:
alpha=0.0001
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) 36.33 % 28.45 % 28.96 %
Car (Orientation) 36.14 % 28.20 % 28.65 %
Car (3D Detection) 11.32 % 9.19 % 9.19 %
Car (Bird's Eye View) 24.80 % 22.05 % 18.70 %
Pedestrian (Detection) 14.05 % 10.12 % 9.99 %
Pedestrian (Orientation) 10.87 % 7.77 % 7.61 %
Pedestrian (3D Detection) 3.62 % 2.13 % 2.21 %
Pedestrian (Bird's Eye View) 4.23 % 2.31 % 2.44 %
Cyclist (Detection) 5.09 % 2.14 % 2.37 %
Cyclist (Orientation) 3.32 % 1.40 % 1.55 %
Cyclist (3D Detection) 0.29 % 0.32 % 0.35 %
Cyclist (Bird's Eye View) 0.88 % 0.88 % 0.88 %
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.
This figure as: png eps pdf txt gnuplot



2D object detection results.
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Orientation estimation results.
This figure as: png eps pdf txt gnuplot



3D object detection results.
This figure as: png eps pdf txt gnuplot



Bird's eye view results.
This figure as: png eps pdf txt gnuplot




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