Adaptive Context-Aware and Structural Correlation Filter for Visual Tracking

Adaptive Context-Aware and Structural Correlation Filter for Visual Tracking

Blog Article

Accurate visual tracking is a challenging issue in computer vision.Correlation filter (CF) based methods are sought in visual tracking based on their efficiency and high performance.Nonetheless, traditional CF-based trackers have insufficient context information, and easily drift in scenes of fast motion or background clutter.Moreover, CF-based trackers are sensitive to partial occlusion, which may reduce their overall performance and even lead to failure in tracking Long Term Care Bed Accessories challenge.

In this paper, we presented an adaptive context-aware (CA) and structural correlation filter for tracking.Firstly, we Control Box propose a novel context selecting strategy to obtain negative samples.Secondly, to gain robustness against partial occlusion, we construct a structural correlation filter by learning both the holistic and local models.Finally, we introduce an adaptive updating scheme by using a fluctuation parameter.

Extensive comprehensive experiments on object tracking benchmark (OTB)-100 datasets demonstrate that our proposed tracker performs favorably against several state-of-the-art trackers.