Method

TuSimple [TuSimple]
https://github.com/precedenceguo/mx-rcnn

Submitted on 9 Nov. 2016 13:18 by
Jian Guo (Beihang University & TuSimple)

Running time:1.6 s
Environment:GPU @ 2.5 Ghz (Python + C/C++)

Method Description:
Parameters:
Latex Bibtex:
@inproceedings{yang2016exploit,
title={Exploit all the layers: Fast and accurate cnn object detector
with scale dependent pooling and cascaded rejection classifiers},
author={Yang, Fan and Choi, Wongun and Lin, Yuanqing},
booktitle={Proceedings of the IEEE Conference on Computer Vision
and Pattern Recognition},
pages={2129--2137},
year={2016}
}
@inproceedings{he2016deep,
title={Deep residual learning for image recognition},
author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and
Sun, Jian},
booktitle={Proceedings of the IEEE conference on computer vision
and pattern recognition},
pages={770--778},
year={2016}
}

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) 90.77 % 90.33 % 82.86 %
Pedestrian (Detection) 86.78 % 77.04 % 72.40 %
Cyclist (Detection) 81.38 % 74.26 % 64.88 %
This table as LaTeX


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



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



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




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