Abstract

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.

Related Publication available at https://arxiv.org/abs/1702.02012

C++ Code

Overall Precision and Success Plots

The experimental results of the tracker on 51 video sequences for one pass evaluation on Online Object Tracking Benchmark [1]

TUNA tracking Results

[1] Yi Wu, Jongwoo Lim, and Ming-Hsuan Yang. 2013. Online Object Tracking: A Benchmark. In Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR '13). IEEE Computer Society, Washington, DC, USA, 2411-2418.