Abstract

In recent years, "FragTrack" has become one of the most cited real time algorithms for visual tracking of an object in a video sequence. However, this algorithm fails when the object model is not present in the image or it is completely occluded, and in long term video sequences. In these sequences, the target object appearance is considerably modified during the time and its comparison with the template established at the first frame is hard to compute. In this work we introduce improvements to the original FragTrack: the management of total object occlusions and the update of the object template. Basically, we use a voting map generated by a non-parametric kernel density estimation strategy that allows us to compute a probability distribution for the distances of the histograms between template and object patches. In order to automatically determine whether the target object is present or not in the current frame, an adaptive threshold is introduced. A Bayesian classifier establishes, frame by frame, the presence of template object in the current frame. The template is partially updated at every frame. We tested the algorithm on well-known benchmark sequences, in which the object is always present, and on video sequences showing total occlusion of the target object to demonstrate the effectiveness of the proposed method.

Autore Pugliese

• Distante Cosimo , Spagnolo Paolo , Mazzeo Pier Luigi

Tutti gli autori

• Adamo F.; Mazzeo P.L.; Spagnolo P.; Distante C.

Titolo volume/Rivista

Proceedings of SPIE, the International Society for Optical Engineering

Anno di pubblicazione

2015

ISSN

0277-786X

ISBN

Non Disponibile

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

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Numero di citazioni Scopus

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Ultimo Aggiornamento Citazioni

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Settori ERC

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Codici ASJC

Non Disponibile