Method

RRC [RRC]
[Anonymous Submission]

Submitted on 27 Apr. 2017 16:01 by
[Anonymous Submission]

Running time:3.6 s
Environment:GPU @ 2.5 Ghz (Python + C/C++)

Method Description:
Accurate Single Stage Detector Using Recurrent
Rolling Convolution (CVPR 2017)
https://arxiv.org/pdf/1704.05776.pdf
Parameters:
Latex Bibtex:
@inproceedings{Ren17CVPR,
author = {Jimmy Ren and Xiaohao Chen and
Jianbo Liu and Wenxiu Sun and Jiahao Pang and
Qiong Yan and Yu-Wing Tai and Li Xu},
title = {Accurate Single Stage Detector Using
Recurrent Rolling Convolution},
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) 90.61 % 90.22 % 87.44 %
Pedestrian (Detection) 84.14 % 75.33 % 70.39 %
Cyclist (Detection) 84.96 % 76.47 % 65.46 %
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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