Method

Objcet deteciton with esimated scale [ODES]
https://github.com/Benzlxs/Object_detection_estimated_sclales

Submitted on 12 Sep. 2018 14:12 by
ben Li (University of New South Wales)

Running time:0.02 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
To cover continuous space
of multiple sizes, we propose the detection method
with the estimated size of anchors.
Parameters:
4 anchors
Latex Bibtex:
wait for publishing

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) 86.82 % 87.10 % 78.32 %
Car (Orientation) 46.22 % 48.06 % 42.43 %
Pedestrian (Detection) 77.95 % 67.25 % 62.28 %
Pedestrian (Orientation) 36.84 % 31.43 % 29.00 %
Cyclist (Detection) 78.51 % 69.80 % 61.32 %
Cyclist (Orientation) 37.75 % 33.74 % 30.34 %
This table as LaTeX


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



Orientation estimation results.
This figure as: png eps pdf txt gnuplot



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



Orientation estimation results.
This figure as: png eps pdf txt gnuplot



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



Orientation estimation results.
This figure as: png eps pdf txt gnuplot




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