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 Shovel are sensitive to partial occlusion, which may reduce their overall performance and even lead to failure in tracking challenge.

In this paper, we presented an adaptive context-aware (CA) and structural Fountains correlation filter for tracking.Firstly, we 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.