Human detection in images via piecewise linear support vector machines

IEEE Transactions on Image Processing

Volumn 22, Issue 2, 2013, Pages 778-789

  Ye, Qixiang ^a   Han, Zhenjun ^a   Jiao, Jianbin ^a   Liu, Jianzhuang ^b,c  

^a UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

^b HUAWEI TECHNOLOGIES CO LTD   (China)

^c CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Classification; object detection; piecewise linear; support vector machine

Indexed keywords

BLOCK ORIENTATION; CLUTTER BACKGROUND; DETECTION ACCURACY; DISCRIMINATIVE FUNCTIONS; FEATURE SPACE; GRADIENT FEATURE; HIGH-DIMENSIONAL FEATURE SPACE; HUMAN BODIES; HUMAN DETECTION; ITERATIVE PROCEDURES; LINEAR SVM; LOCAL LINEAR; MULTI-POSTURE; MULTI-VIEWS; NONLINEAR CLASSIFICATION; OBJECT DETECTION; PIECE-WISE; PIECEWISE LINEAR; STATE OF THE ART; SVM MODEL;

CLASSIFICATION (OF INFORMATION); DETECTORS; ITERATIVE METHODS; OBJECT RECOGNITION; PIECEWISE LINEAR TECHNIQUES;

SUPPORT VECTOR MACHINES;

ANIMAL; ARTICLE; AUTOMATED PATTERN RECOGNITION; DAILY LIFE ACTIVITY; FACTUAL DATABASE; HUMAN; IMAGE PROCESSING; METHODOLOGY; SUPPORT VECTOR MACHINE; VIDEORECORDING;

ACTIVITIES OF DAILY LIVING; ANIMALS; DATABASES, FACTUAL; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED; SUPPORT VECTOR MACHINES; VIDEO RECORDING;

MLCS; MLOWN;

EID: 84872340829     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2012.2222901     Document Type: Article

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