Dense descriptor for visual tracking and robust update model strategy

Autor: Mazzeo, Pier, Spagnolo, Paolo, Leo, Marco, Carcagnì, Pierluigi, Del Coco, Marco, Distante, Cosimo
Zdroj: Journal of Ambient Intelligence and Humanized Computing; 20240101, Issue: Preprints p1-11, 11p
Abstrakt: Context analysis is a research field that is attracting growing interest in recent years, especially due to the encouraging results carried out by the semantic-based approach. Anyway, semantic strategies entail the use of trackers capable to show robustness to long-term occlusions, viewpoint changes and identity swap that represent the main problem of many tracking-by-detection solutions. This paper proposes a robust tracking-by-detection framework based on dense SIFT descriptors in combination with an ad-hoc target appearance model update able to overtake the discussed issues. The obtained performances show how our tracker competes with state-of-the-art results and manages occlusions, clutter, changes of scale, rotation and appearance, better than competing tracking methods.
Databáze: Supplemental Index
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