Square patch feature: Faster weak-classifier for robust object detection

Mustafah, Yasir M., Bigdeli, Abbas, Azman, Amelia W., Dadgostar, Farhad and Lovell, Brian C. (2010). Square patch feature: Faster weak-classifier for robust object detection. In: 11th International Conference on Control Automation Robotics & Vision (ICARCV), 2010. 11th International Conference on Control Automation Robotics & Vision (ICARCV), 2010, Singapore, (2073-2077). 7-10 December 2010. doi:10.1109/ICARCV.2010.5707809

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Author Mustafah, Yasir M.
Bigdeli, Abbas
Azman, Amelia W.
Dadgostar, Farhad
Lovell, Brian C.
Title of paper Square patch feature: Faster weak-classifier for robust object detection
Conference name 11th International Conference on Control Automation Robotics & Vision (ICARCV), 2010
Conference location Singapore
Conference dates 7-10 December 2010
Proceedings title 11th International Conference on Control Automation Robotics & Vision (ICARCV), 2010
Journal name 11th International Conference On Control, Automation, Robotics and Vision (icarcv 2010)
Place of Publication Piscataway, NJ, United States
Publisher IEEE (Institute for Electrical and Electronic Engineers)
Publication Year 2010
Sub-type Fully published paper
DOI 10.1109/ICARCV.2010.5707809
ISBN 9781424478132
Start page 2073
End page 2077
Total pages 5
Collection year 2011
Language eng
Abstract/Summary This paper presents a novel generic weak classifier for object detection called "Square Patch Feature". The speed and overall performance of a detector utilising Square Patch features in comparison to other weak classifiers shows improvement. Each weak classifier is based on the difference between two or four fixed size square patches in an image. A pre-calculated representation of the image called "patch image" is required to accelerate the weak classifiers computation. The computation requires fewer arithmetic operations and fewer accesses to the main memory in comparison to the well known Viola-Jones Haar-like classifier. In addition to the faster computation, the weak classifier can be extended for in-plane rotation, where each square patch can be rotated to detect in-plane rotated objects. The results of the experiments on the MIT CBCL Face dataset show that a Square Patch Feature classifier is as accurate as the Viola-Jones Haar-like classifier, and when implemented on hardware (i.e. FPGA), it is almost 2 times faster. ©2010 IEEE.
Keyword Object detection
Rotation invariance
Weak classifier
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Article number 5707809

 
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Created: Fri, 13 May 2011, 22:31:00 EST by Ms Deborah Brian on behalf of School of Information Technol and Elec Engineering