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Support vector tracking.
IEEE Trans Pattern Anal Mach Intell. 2004 Aug; 26(8):1064-72.IT

Abstract

Support Vector Tracking (SVT) integrates the Support Vector Machine (SVM) classifier into an optic-flow-based tracker. Instead of minimizing an intensity difference function between successive frames, SVT maximizes the SVM classification score. To account for large motions between successive frames, we build pyramids from the support vectors and use a coarse-to-fine approach in the classification stage. We show results of using SVT for vehicle tracking in image sequences.

Authors+Show Affiliations

Avidan S
MobilEye Vision Technologies LTD, Jerusalem, Israel. avidan@mobileye.com

MeSH

AlgorithmsArtificial IntelligenceComputer GraphicsImage EnhancementImage Interpretation, Computer-AssistedImaging, Three-DimensionalInformation Storage and RetrievalMovementNumerical Analysis, Computer-AssistedPattern Recognition, AutomatedReproducibility of ResultsSensitivity and SpecificitySignal Processing, Computer-AssistedSubtraction TechniqueVideo Recording

Pub Type(s)

Comparative Study
Evaluation Study
Journal Article
Validation Study

Language

eng

PubMed ID

15641735

Citation

Avidan, Shai. "Support Vector Tracking." IEEE Transactions On Pattern Analysis and Machine Intelligence, vol. 26, no. 8, 2004, pp. 1064-72.
Avidan S. Support vector tracking. IEEE Trans Pattern Anal Mach Intell. 2004;26(8):1064-72.
Avidan, S. (2004). Support vector tracking. IEEE Transactions On Pattern Analysis and Machine Intelligence, 26(8), 1064-72.
Avidan S. Support Vector Tracking. IEEE Trans Pattern Anal Mach Intell. 2004;26(8):1064-72. PubMed PMID: 15641735.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Support vector tracking. A1 - Avidan,Shai, PY - 2005/1/12/pubmed PY - 2005/2/11/medline PY - 2005/1/12/entrez SP - 1064 EP - 72 JF - IEEE transactions on pattern analysis and machine intelligence JO - IEEE Trans Pattern Anal Mach Intell VL - 26 IS - 8 N2 - Support Vector Tracking (SVT) integrates the Support Vector Machine (SVM) classifier into an optic-flow-based tracker. Instead of minimizing an intensity difference function between successive frames, SVT maximizes the SVM classification score. To account for large motions between successive frames, we build pyramids from the support vectors and use a coarse-to-fine approach in the classification stage. We show results of using SVT for vehicle tracking in image sequences. SN - 0162-8828 UR - https://www.unboundmedicine.com/medline/citation/15641735/Support_vector_tracking_ DB - PRIME DP - Unbound Medicine ER -