Submitted on 8 Mar. 2017 09:32 by
Florian CHABOT (cea)

Running time:0.7 s
Environment:GPU @ 2.5 Ghz (Python + C/C++)

Method Description:
Latex Bibtex:
title = {Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image},
author = { F. Chabot and M. Chaouch and J. Rabarisoa and C. Teulière and T. Chateau},
booktitle = {CVPR},
year = {2017}

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) 98.89 % 93.50 % 83.21 %
Car (Orientation) 98.83 % 93.31 % 82.95 %
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

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