Online single and multiple analysis dictionary learning-based approach for visual object tracking

Abstract

In recent years, various synthesis dictionary learning-based techniques have been applied for the task of visual object tracking. However, there are challenges in the representation ability of the target and the background using a synthesis dictionary. Owing to such challenges, the target in a given video sequence is not tracked very efficiently. We propose an online analysis dictionary learning framework for visual tracking. Our main motivation stems from the fact that analysis dictionary is able to capture significantly more variability in the data as compared to a synthesis dictionary of the same dimensions. We make use of single and multiple analysis dictionaries in our work. In order to leverage the advantage of multiple dictionaries, we adaptively fuse them. To the best of our knowledge, this is the first work that uses analysis dictionary for visual tracking. We use the entire OTB-50 dataset to evaluate the performance of proposed trackers. Our experiments show a significant improvement over existing synthesis dictionary learning approaches. This makes our approach much more preferable over the conventional approaches that use synthesis dictionary learning model for visual tracking.