Hidden part models for human action recognition: Probabilistic versus max margin

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 33, Issue 7, 2011, Pages 1310-1323

  Wang, Yang ^a   Mori, Greg ^b  

^a University of Illinois at Urbana Champaign   (United States)

^b SIMON FRASER UNIVERSITY   (Canada)

Author keywords

discriminative learning; hidden conditional random field; Human action recognition; max margin; part based model

Indexed keywords

DISCRIMINATIVE LEARNING; HIDDEN CONDITIONAL RANDOM FIELD; HUMAN ACTION RECOGNITION; MAX MARGIN; PART-BASED MODEL;

COMPUTER VISION; GESTURE RECOGNITION; MOTION ESTIMATION; OBJECT RECOGNITION;

FEATURE EXTRACTION;

ACTIMETRY; ALGORITHM; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; HUMAN; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; MACHINE LEARNING; MARKOV CHAIN; MOVEMENT (PHYSIOLOGY); PHYSIOLOGY; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; VIDEORECORDING; WHOLE BODY IMAGING;

ACTIGRAPHY; ALGORITHMS; COMPUTER SIMULATION; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MACHINE LEARNING; MARKOV CHAINS; MODELS, STATISTICAL; MOVEMENT; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE; VIDEO RECORDING; WHOLE BODY IMAGING;

EID: 79957467077     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2010.214     Document Type: Article

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