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On Detection of Multiple Object Instances Using Hough Transforms
Sept. 2012 (vol. 34 no. 9)
pp. 1773-1784
ASCII Text
x 
 
O. Barinova, V. Lempitsky, P. Kholi, "On Detection of Multiple Object Instances Using Hough Transforms," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 9, pp. 1773-1784, Sept., 2012.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2012.79,
author = {O. Barinova and V. Lempitsky and P. Kholi},
title = {On Detection of Multiple Object Instances Using Hough Transforms},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {34},
number = {9},
issn = {0162-8828},
year = {2012},
pages = {1773-1784},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2012.79},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - On Detection of Multiple Object Instances Using Hough Transforms
IS - 9
SN - 0162-8828
SP1773
EP1784
EPD - 1773-1784
A1 - O. Barinova,
A1 - V. Lempitsky,
A1 - P. Kholi,
PY - 2012
KW - probability
KW - Hough transforms
KW - object detection
KW - pedestrians
KW - pedestrian detection
KW - multiple object instances
KW - Hough transforms
KW - nonmaxima suppression
KW - mode seeking
KW - Hough images
KW - postprocessing
KW - parameter tuning
KW - probabilistic framework
KW - object detection
KW - multiple peak identification
KW - nonmaximum suppression heuristics
KW - detection accuracy
KW - classical task
KW - straight line detection
KW - modern category-level detection
KW - Transforms
KW - Probabilistic logic
KW - Object detection
KW - Image edge detection
KW - Joints
KW - Cognition
KW - Random variables
KW - scene understanding.
KW - Hough transforms
KW - object detection in images
KW - line detection
VL - 34
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
O. Barinova, Lomonosov Moscow State Univ., Moscow, Russia
V. Lempitsky, Yandex, Moscow, Russia
P. Kholi, Microsoft Res., Cambridge, UK
Hough transform-based methods for detecting multiple objects use nonmaxima suppression or mode seeking to locate and distinguish peaks in Hough images. Such postprocessing requires the tuning of many parameters and is often fragile, especially when objects are located spatially close to each other. In this paper, we develop a new probabilistic framework for object detection which is related to the Hough transform. It shares the simplicity and wide applicability of the Hough transform but, at the same time, bypasses the problem of multiple peak identification in Hough images and permits detection of multiple objects without invoking nonmaximum suppression heuristics. Our experiments demonstrate that this method results in a significant improvement in detection accuracy both for the classical task of straight line detection and for a more modern category-level (pedestrian) detection problem.

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Index Terms:
probability,Hough transforms,object detection,pedestrians,pedestrian detection,multiple object instances,Hough transforms,nonmaxima suppression,mode seeking,Hough images,postprocessing,parameter tuning,probabilistic framework,object detection,multiple peak identification,nonmaximum suppression heuristics,detection accuracy,classical task,straight line detection,modern category-level detection,Transforms,Probabilistic logic,Object detection,Image edge detection,Joints,Cognition,Random variables,scene understanding.,Hough transforms,object detection in images,line detection
Citation:
O. Barinova, V. Lempitsky, P. Kholi, "On Detection of Multiple Object Instances Using Hough Transforms," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 9, pp. 1773-1784, Sept. 2012, doi:10.1109/TPAMI.2012.79
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