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Extraction of Long-Duration Moving Object Trajectories from Curtailed Tracks

Conference paper
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Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 704)

Abstract

Object tracking remains one of the critical challenges in visual surveillance. It is difficult to track each moving object in a crowded scene. This paper proposed a new approach to track moving objects for longer duration. First, key points are tracked for short duration using state-of-the-art feature tracker. Next, the features are grouped and linked in spatiotemporal domain. Finally, we create a single trajectory for each object or a group of similar objects. We have tested the method on publicly available video datasets where more than 100 people were moving randomly. The results reveal that the proposed method can be highly effective to extract long-duration trajectories from the curtailed tracklets obtained using short-duration feature tracker.

Keywords

Object Motion Tracklets Crowded Scenes Kanade-Lucas-Tomasi Feature Tracker Short Tracklets 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Haldia Institute of TechnologyHaldiaIndia
  2. 2.IIT BhubaneswarBhubaneswarIndia
  3. 3.National Institute of Technology DurgapurDurgapurIndia
  4. 4.Indian Institute of Technology RoorkeeRoorkeeIndia

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