Title :
Cascade Linear SVM for Object Detection
Author :
Song, Jinze ; Wu, Tao ; An, Ping
Author_Institution :
Coll. of Mechatron. Eng. & Autom., Nat. Univ. of Defence Technol., Changcha
Abstract :
This paper develops a cascade of linear SVM classifiers for fast object detection. The learning problem of every node in the cascade structure is described as a new quadratic programming problem in the frame of SVM, which makes every linear classifier achieve very high detection rate but only moderate false positive rate. The real experiment shows that this method enjoys good generalization capacity and much fast speed compared with the traditional SVMs.
Keywords :
image classification; learning (artificial intelligence); object detection; quadratic programming; support vector machines; cascade linear SVM classifier; machine learning; object detection; quadratic programming; Automation; Detectors; Educational institutions; Face detection; Los Angeles Council; Mechatronics; Object detection; Risk management; Support vector machine classification; Support vector machines; Cascade; object detection; support vector machine (SVM);
Conference_Titel :
Young Computer Scientists, 2008. ICYCS 2008. The 9th International Conference for
Conference_Location :
Hunan
Print_ISBN :
978-0-7695-3398-8
Electronic_ISBN :
978-0-7695-3398-8
DOI :
10.1109/ICYCS.2008.173