Anzeige
Erschienen in:
2017 | OriginalPaper | Buchkapitel
An Efficient and Robust Visual Tracking via Color-Based Context Prior Model
verfasst von : Chunlei Yu, Baojun Zhao, Zengshuo Zhang, Maowen Li
Erschienen in: Image and Graphics
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
Real-time object tracking has been widely applied to time-critical multimedia fields such as surveillance and human-computer interaction. It is a challenge to balance accuracy and speed in tracking. Spatio-Temporal Context tracker (STC) formulates the spatio-temporal relationship between the object and its surrounding background, and achieves good performance in accuracy and speed. However, the context prior model only utilizes the grayscale feature which is not efficient. When the target is not obvious in the context, or the context exists a similar interference compared to the target, STC tracker drifts from the target. To solve the problem, we exploit the standard color histograms of the context to build a discriminative context prior model. More specifically, we utilize an effective lookup-table to compute the prior context model at a low computational cost. Finally, extensive experiments on challenging sequences show the effectiveness and robustness of our proposed method.