Dynamic Saliency-Aware Regularization for Correlation Filter-Based Object Tracking

doi:10.1109/TIP.2019.2895411

Dynamic Saliency-Aware Regularization for Correlation Filter-Based Object Tracking

2019-07-01

101

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AI

DOI：10.1109/TIP.2019.2895411

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With a good balance between tracking accuracy and speed, correlation filter (CF) has become one of the hest object tracking frameworks, based on which many successful trackers have been developed. Recently, spatially regularized CF tracking (SRDCF) has been developed to remedy the annoying boundary effects of CF tracking, thus further boosting the tracking performance. However, SRDCF uses a fixed spatial regularization map constructed from a loose bounding box and its performance inevitably degrades when the target or background show significant variations, such as object deformation or occlusion. To address this problem, we propose a new dynamic saliency-aware regularized CF tracking (DSAR-CF) scheme. In DSAR-CF, a simple yet effective energy function, which reflects the object saliency and tracking reliability in the spatial-temporal domain, is defined to guide the online updating of the regularization weight map using an efficient levelset algorithm. Extensive experiments validate that the proposed DSAR-CF leads to better performance in terms of accuracy and speed than the original SRDCF.

Keyword:

Correlation filter

object tracking

saliency

dynamic spatial regularization

level-set optimization

Dynamic Saliency-Aware Regularization for Correlation Filter-Based Object Tracking

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