Spatio-temporal auxiliary particle filtering with ℓ1-Norm- Based Appearance Model Learning for Robust Visual Tracking

IEEE Transactions on Image Processing

Volumn 22, Issue 2, 2013, Pages 511-522

  Kim, Du Yong ^a,b   Jeon, Moongu ^a  

^a Gwangju Institute of Science and Technology (GIST)   (South Korea)

^b UNIVERSITY OF WESTERN AUSTRALIA   (Australia)

Author keywords

Particle filtering; subspace learning; visual tracking

Indexed keywords

ABRUPT MOTION; APPEARANCE MODELS; AUXILIARY PARTICLE FILTERING; COMPUTATIONALLY EFFICIENT; OBJECT APPEARANCE; OUT-OF-PLANE MOTION; PARTICLE FILTERING; PARTICLE FILTERING ALGORITHMS; PRINCIPAL COMPONENTS; RECURSIVE FORMS; SLIDING WINDOW; SPATIO TEMPORAL FILTERING; SPATIO-TEMPORAL; SUBSPACE LEARNING; VIDEO SEQUENCES; VISUAL TRACKING;

MULTIUSER DETECTION; SIGNAL FILTERING AND PREDICTION; TRACKING (POSITION);

MONTE CARLO METHODS;

EID: 84872353390     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2012.2218824     Document Type: Article

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