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Multistage Particle Windows for Fast and Accurate Object Detection
Aug. 2012 (vol. 34 no. 8)
pp. 1589-1604
ASCII Text
x 
 
A. Prati, G. Gualdi, R. Cucchiara, "Multistage Particle Windows for Fast and Accurate Object Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 8, pp. 1589-1604, Aug., 2012.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2011.247,
author = {A. Prati and G. Gualdi and R. Cucchiara},
title = {Multistage Particle Windows for Fast and Accurate Object Detection},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {34},
number = {8},
issn = {0162-8828},
year = {2012},
pages = {1589-1604},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2011.247},
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 - Multistage Particle Windows for Fast and Accurate Object Detection
IS - 8
SN - 0162-8828
SP1589
EP1604
EPD - 1589-1604
A1 - A. Prati,
A1 - G. Gualdi,
A1 - R. Cucchiara,
PY - 2012
KW - search problems
KW - Bayes methods
KW - feature extraction
KW - Gaussian processes
KW - grid computing
KW - image classification
KW - image sampling
KW - Monte Carlo methods
KW - object detection
KW - sliding window detection
KW - multistage particle windows
KW - accurate object detection
KW - fast object detection
KW - sliding window search
KW - grid-distributed patches
KW - binary classifier
KW - statistical-based search
KW - Monte Carlo sampling
KW - likelihood density function
KW - Gaussian kernels
KW - multistage strategy
KW - Bayesian-recursive framework
KW - temporal coherency
KW - face detection
KW - pedestrian detection
KW - Face
KW - Feature extraction
KW - Accuracy
KW - Object detection
KW - Support vector machines
KW - Search problems
KW - Face detection
KW - coarse-to-fine search refinement.
KW - Efficient object detection
KW - pedestrian detection
VL - 34
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
A. Prati, Dept. of Eng. Sci. & Methods, Univ. of Modena & Reggio Emilia, Reggio Emilia, Italy
G. Gualdi, Dept. of Inf. Eng., Univ. of Modena & Reggio Emilia, Modena, Italy
R. Cucchiara, Dept. of Inf. Eng., Univ. of Modena & Reggio Emilia, Modena, Italy
The common paradigm employed for object detection is the sliding window (SW) search. This approach generates grid-distributed patches, at all possible positions and sizes, which are evaluated by a binary classifier: The tradeoff between computational burden and detection accuracy is the real critical point of sliding windows; several methods have been proposed to speed up the search such as adding complementary features. We propose a paradigm that differs from any previous approach since it casts object detection into a statistical-based search using a Monte Carlo sampling for estimating the likelihood density function with Gaussian kernels. The estimation relies on a multistage strategy where the proposal distribution is progressively refined by taking into account the feedback of the classifiers. The method can be easily plugged into a Bayesian-recursive framework to exploit the temporal coherency of the target objects in videos. Several tests on pedestrian and face detection, both on images and videos, with different types of classifiers (cascade of boosted classifiers, soft cascades, and SVM) and features (covariance matrices, Haar-like features, integral channel features, and histogram of oriented gradients) demonstrate that the proposed method provides higher detection rates and accuracy as well as a lower computational burden w.r.t. sliding window detection.

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Index Terms:
search problems,Bayes methods,feature extraction,Gaussian processes,grid computing,image classification,image sampling,Monte Carlo methods,object detection,sliding window detection,multistage particle windows,accurate object detection,fast object detection,sliding window search,grid-distributed patches,binary classifier,statistical-based search,Monte Carlo sampling,likelihood density function,Gaussian kernels,multistage strategy,Bayesian-recursive framework,temporal coherency,face detection,pedestrian detection,Face,Feature extraction,Accuracy,Object detection,Support vector machines,Search problems,Face detection,coarse-to-fine search refinement.,Efficient object detection,pedestrian detection
Citation:
A. Prati, G. Gualdi, R. Cucchiara, "Multistage Particle Windows for Fast and Accurate Object Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 8, pp. 1589-1604, Aug. 2012, doi:10.1109/TPAMI.2011.247
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