Extended fast compressive tracking with weighted multi-frame template matching for fast motion tracking

摘要

The work presented in this letter extends upon state of the art visual object tracking algorithms, the Real-time Compressive Tracker and the Fast Compressive Tracker, increasing the overall tracking accuracy at a minimal computational cost and reduction in frame rate. A template matching processing stage is incorporated in order to increase the robustness of the algorithm while maintaining a frame rate well within the requirements for real time operation. We utilise a weighted multi-frame similarity metric, template matching a bank of the top classifier outputs against the ground truth bounding box and a recently stored target bounding box to select the appropriate target location in the following frame. Unlike the original algorithm, the proposed method utilises more of the available data to make more informed tracking decisions than purely using the highest classifier output. Multiple similarity metrics have been employed in the template matching stage to compare their performance on a range of commonly used publicly available image sequences. The extended algorithm clearly demonstrated an increase in the overall performance while maintaining a high frame-rate. (C) 2015 Elsevier B.V. All rights reserved.