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Machine Vision and Applications

, Volume 29, Issue 2, pp 247–261 | Cite as

Tracking using Numerous Anchor Points

  • Tanushri ChakravortyEmail author
  • Guillaume-Alexandre Bilodeau
  • Éric Granger
Original Paper
  • 526 Downloads

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 help 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.

Keywords

Visual object tracking Keypoints Star-like structure Gaussian Voting Model-free tracker 

Notes

Acknowledgements

This work was supported in part by FRQ-NT team Grant #167442 and by REPARTI (Regroupement pour l’étude des environnements partagés intelligents répartis) FRQ-NT strategic cluster.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.LITIV Lab., Department of Computer and Software EngineeringPolytechnique MontrealQuebecCanada
  2. 2.LIVIA, École de technologie supérieureUniversité du Québec, MontrealQuebecCanada

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