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A Real-Time Deformable Detector
February 2012 (vol. 34 no. 2)
pp. 225-239
ASCII Text
x 
 
K. Ali, F. Fleuret, D. Hasler, P. Fua, "A Real-Time Deformable Detector," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 2, pp. 225-239, February, 2012.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2011.117,
author = {K. Ali and F. Fleuret and D. Hasler and P. Fua},
title = {A Real-Time Deformable Detector},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {34},
number = {2},
issn = {0162-8828},
year = {2012},
pages = {225-239},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2011.117},
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 - A Real-Time Deformable Detector
IS - 2
SN - 0162-8828
SP225
EP239
EPD - 225-239
A1 - K. Ali,
A1 - F. Fleuret,
A1 - D. Hasler,
A1 - P. Fua,
PY - 2012
KW - real-time systems
KW - object detection
KW - pose estimation
KW - face images
KW - real-time deformable detector
KW - object detection
KW - AdaBoost procedure
KW - pose estimators
KW - video sequences
KW - aerial images
KW - Feature extraction
KW - Image processing
KW - Object detection
KW - Training data
KW - Image edge detection
KW - Machine learning
KW - Learning systems
KW - object detection.
KW - Image processing and computer vision
KW - machine learning
VL - 34
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
K. Ali, EPFL IC CVLAB, Lausanne, Switzerland
F. Fleuret, Centre du Parc, Idiap Res. Inst., Martigny, Switzerland
D. Hasler, CSEM SA, Neuchdtel, Switzerland
P. Fua, EPFL IC CVLAB, Lausanne, Switzerland
We propose a new learning strategy for object detection. The proposed scheme forgoes the need to train a collection of detectors dedicated to homogeneous families of poses, and instead learns a single classifier that has the inherent ability to deform based on the signal of interest. We train a detector with a standard AdaBoost procedure by using combinations of pose-indexed features and pose estimators. This allows the learning process to select and combine various estimates of the pose with features able to compensate for variations in pose without the need to label data for training or explore the pose space in testing. We validate our framework on three types of data: hand video sequences, aerial images of cars, and face images. We compare our method to a standard boosting framework, with access to the same ground truth, and show a reduction in the false alarm rate of up to an order of magnitude. Where possible, we compare our method to the state of the art, which requires pose annotations of the training data, and demonstrate comparable performance.

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Index Terms:
real-time systems,object detection,pose estimation,face images,real-time deformable detector,object detection,AdaBoost procedure,pose estimators,video sequences,aerial images,Feature extraction,Image processing,Object detection,Training data,Image edge detection,Machine learning,Learning systems,object detection.,Image processing and computer vision,machine learning
Citation:
K. Ali, F. Fleuret, D. Hasler, P. Fua, "A Real-Time Deformable Detector," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 2, pp. 225-239, Feb. 2012, doi:10.1109/TPAMI.2011.117
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