  In this paper, an online adaptive model-free tracker is proposed to track
single objects in video sequences to deal with real-world tracking challenges
like low-resolution, object deformation, occlusion and motion blur. The novelty
lies in the construction of a strong appearance model that captures features
from the initialized bounding box and then are assembled into anchor-point
features. These features memorize the global pattern of the object and have an
internal star graph-like structure. These features are unique and flexible and
helps tracking generic and deformable objects with no limitation on specific
objects. In addition, the relevance of each feature is evaluated online using
short-term consistency and long-term consistency. These parameters are adapted
to retain consistent features that vote for the object location and that deal
with outliers for long-term tracking scenarios. Additionally, voting in a
Gaussian manner helps in tackling inherent noise of the tracking system and in
accurate object localization. Furthermore, the proposed tracker uses pairwise
distance measure to cope with scale variations and combines pixel-level binary
features and global weighted color features for model update. Finally,
experimental results on a visual tracking benchmark dataset are presented to
demonstrate the effectiveness and competitiveness of the proposed tracker.
