Object Tracking With Spatial Context Model
Juntao Sun, Shunli Zhang, Li Zhang · IEEE Signal Processing Letters · 2016
In object tracking, building a reliable appearance model can greatly improve the performance. In this letter, we propose a novel method that uses the spatial context to help tracking based on support vector machines (SVMs). The spatial context is decomposed into different subregions that include some parts of both the target and the background. We build appearance submodels for each group of subregions and combine them to get a robust appearance model, which can help to handle some complex problems, e.g., occlusion and deformation. Besides, we add an update strategy to retain the accuracy of the appearance model. A large number of experiments on various challenging videos demonstrate that our method outperforms many other state-of-the-art methods.