Support vector tracking
- 21 June 2004
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Pattern Analysis and Machine Intelligence
- Vol. 26 (8), 1064-1072
- https://doi.org/10.1109/tpami.2004.53
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.This publication has 12 references indexed in Scilit:
- Rapid object detection using a boosted cascade of simple featuresPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Comparison of classifier methods: a case study in handwritten digit recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Training support vector machines: an application to face detectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Computationally efficient face detectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- A Trainable System for Object DetectionInternational Journal of Computer Vision, 2000
- Example-based learning for view-based human face detectionIEEE Transactions on Pattern Analysis and Machine Intelligence, 1998
- Support-vector networksMachine Learning, 1995
- The Nature of Statistical Learning TheoryPublished by Springer Science and Business Media LLC ,1995
- Tracking and Recognition of Face SequencesPublished by Springer Science and Business Media LLC ,1995
- Hierarchical Model-Based Motion EstimationPublished by Springer Science and Business Media LLC ,1993