Method

Boosting based Detection using Scale Invariant Multimodal Multiresolution Filtered Features [la][fl] [MM-MRFC]


Submitted on 22 Nov. 2016 16:23 by
Arthur Costea (Technical University of Cluj-Napoca)

Running time:0.05 s
Environment:GPU @ 2.5 Ghz (C/C++)

Method Description:
A. D. Costea, R. Varga, S. Nedevschi, "Fast
Boosting based Detection using Scale Invariant
Multimodal Multiresolution Filtered Features",
CVPR 2017.

http://openaccess.thecvf.com/content_cvpr_2017/pap
ers/Costea_Fast_Boosting_Based_CVPR_2017_paper.pdf
Parameters:
Please check the paper.
Latex Bibtex:
@inproceedings{Costea2017CVPR, title = {Fast
Boosting based Detection using Scale Invariant
Multimodal Multiresolution Filtered Features},
author = {Arthur Daniel Costea and Robert Varga
and Sergiu Nedevschi}, 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) 90.93 % 88.20 % 78.02 %
Pedestrian (Detection) 82.37 % 69.96 % 64.76 %
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




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