Method

PDV with subcategory [PDV-Subcat]


Submitted on 19 May. 2018 15:32 by
Jifeng Shen (Jiangsu university)

Running time:7 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
pixel differential feature with 25 subcategory
Parameters:
s=1,r=2,25 Subcategories,nOct=1
Latex Bibtex:
@article{Shen2017PR,
title = "A novel pixel neighborhood
differential
statistic feature for pedestrian and face
detection ",
journal = "Pattern Recognition ",
volume = "63",
number = "",
pages = "127 - 138",
year = "2017",
note = "",
issn = "0031-3203",
doi =
"http://dx.doi.org/10.1016/j.patcog.2016.09.010
",
url =
"http://www.sciencedirect.com/science/article/p
ii/
S0031320316302710",
author = "Jifeng Shen and Xin Zuo and Jun Li
and
Wankou Yang and Haibin Ling",
}

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) 77.33 % 63.15 % 49.75 %
This table as LaTeX


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




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