Real-time compressive tracking based on online feature selection

Abstract

As the projection matrix is only generated in the initial stage and kept constant in subsequent processing, so when the object is occluded or its appearance changes, this will result in drifting or tracking lost. To address this problem, this paper proposes a real-time compressive tracking algorithm based on online feature selection. First, the feature pools are constructed. Then, features with high confidence score are selected from the feature pool by a confidence evaluation strategy. These discriminating features and their corresponding confidences are integrated to construct a classifier. Finally, tracking processing is carried on by the classifier. Tracking performance of our algorithm compares with that of the original algorithm on several public testing video sequences. Our algorithm improves on the tracking accuracy and robustness; furthermore, the processing speed is approximately 25 frames per second. It meets the requirements of real-time tracking.