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Clustering Local Motion Estimates for Robust and Efficient Object Tracking

  • Mario Edoardo Maresca
  • Alfredo PetrosinoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8926)

Abstract

We present a new short-term tracking algorithm called Best Displacement Flow (BDF). This approach is based on the idea of ‘Flock of Trackers’ with two main contributions. The first contribution is the adoption of an efficient clustering approach to identify what we term the ‘Best Displacement’ vector, used to update the object’s bounding box. This clustering procedure is more robust than the median filter to high percentage of outliers. The second contribution is a procedure that we term ‘Consensus-Based Reinitialization’ used to reinitialize trackers that have previously been classified as outliers. For this reason we define a new tracker state called ‘transition’ used to sample new trackers in according to the current inlier trackers.

Keywords

Visual object tracking Optical flow Motion-based Texture-less tracking 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Science and TechnologyUniversity of Naples ParthenopeNaplesItaly

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