Visual object tracking with multi-scale superpixels and color-feature guided kernelized correlation filters

摘要

While discriminative correlation filter based tracking algorithms have achieved competitive results and demonstrated successfully, there still remain some challenges, such as handling the scenarios of scale variation, fast motion, etc. A region of interest with a fixed size, which is usually used by discriminative correlation filter based tracking algorithms to train correlation filter and track object, makes the trackers hard to deal with the challenges of fast motion and scale variation. It also restricts the use of object structure information. In this paper, we propose a Multi-Scale Superpixels and Color Feature Guided Kemelized Correlation Filters (MSSCF-KCF) to deal with the problems mentioned above. Firstly, we treat tracking procedure as optimizing the combination of components of an object, and propose a multi-scale superpixel method to segment object image based on a proposed global confidence mask which determines the center and size of object patches automatically. Then, KCF is embedded into Bayesian filter framework, in which the proposed color guided confidence map is viewed as an observation model, to expand the tracking range and improve the performance when handling fast motion. Finally, we propose a center distance matrix of each object patch to use structure information of object. A novel min-max criterion as well as drop-out strategy is used to search the optimal combination and estimate the state of object, which also helps the tracker to cope with scale variation. Besides, monitoring update strategy is proposed to monitor tacking operation and adjust parameters dynamically. The proposed algorithm is tested on a self-made dataset and some challenging sequences with a novel evaluation criterion. The results demonstrate the feasibility and effectiveness compared with the state-of-the-art methods.