Method

SquaresChnFtrs, strong baseline in "Seeking the strongest rigid detector" [SquaresICF]
https://bitbucket.org/rodrigob/doppia/

Submitted on 26 Jun. 2014 19:06 by
Mohamed Omran (Max-Planck-Institute for Informatics)

Running time:1 s
Environment:GPU @ >3.5 Ghz (C/C++)

Method Description:
A variant of the Integral Channel Features
detector. It uses only HOG+LUV features, with
square pooling regions.
Parameters:
Same parameters as the strong baseline described in
the paper, except that we train with five
bootstrapping rounds, instead of two.
Latex Bibtex:
@INPROCEEDINGS{Benenson2013Cvpr,
author = {R. Benenson and M. Mathias and T.
Tuytelaars and L. {Van Gool}},
title = {Seeking the strongest rigid detector},
booktitle = {CVPR},
year = {2013}
}

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
Pedestrian (Detection) 57.08 % 42.61 % 37.85 %
This table as LaTeX

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




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