Representation is a fundamental problem in object tracking. Conventional methods track the target by describing its local or global appearance. In this paper we present that, besides the two paradigms, the composition of local region histograms can also provide diverse and important object cues. We use cells to extract local appearance, and construct complex cells to integrate the information from cells. With different spatial arrangements of cells, complex cells can explore various contextual information at multiple scales, which is important to improve the tracking performance. We also develop a novel template-matching algorithm for object tracking, where the template is composed of temporal varying cells and has two layers to capture the target and background appearance respectively. An adaptive weight is associated with each complex cell to cope with occlusion as well as appearance variation. A fusion weight is associated with each complex cell type to preserve the global distinctiveness. Our algorithm is evaluated on 25 challenging sequences, and the results not only confirm the contribution of each component in our tracking system, but also outperform other competing trackers.
