Method

Roadstar_Perception [Roadstar.ai]


Submitted on 18 Apr. 2018 11:30 by
Tongyi Cao (Umass Amherst)

Running time:0.08 s
Environment:GPU @ 2.0 Ghz (Python)

Method Description:
We propose a novel one stage method based on SSD. Author Tongyi Cao, Maosheng Ye and Guang Zhou.
Parameters:
TBA
Latex Bibtex:
TBA

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) 82.93 % 74.84 % 67.18 %
Car (Orientation) 32.87 % 29.44 % 26.77 %
Car (3D Detection) 48.60 % 44.00 % 40.05 %
Car (Bird's Eye View) 88.55 % 83.57 % 77.87 %
Pedestrian (Detection) 47.02 % 42.72 % 42.36 %
Pedestrian (Orientation) 24.51 % 22.18 % 21.91 %
Pedestrian (3D Detection) 24.61 % 23.28 % 21.97 %
Pedestrian (Bird's Eye View) 50.74 % 46.18 % 43.36 %
Cyclist (Detection) 75.74 % 64.48 % 57.79 %
Cyclist (Orientation) 32.23 % 27.58 % 24.88 %
Cyclist (3D Detection) 55.85 % 45.51 % 41.48 %
Cyclist (Bird's Eye View) 71.89 % 59.11 % 52.85 %
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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