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Improved CAMshift Based on Supervised Learning

  • Nur Ariffin Mohd Zin
  • Siti Norul Huda Sheikh Abdullah
  • Azizi Abdullah
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 208)

Abstract

CAMshift algorithm refers on back-projected distribution of target object’s colour to locate the location of the target object in the subsequent frame. However, this mechanism becomes inaccurate when one or more foreign objects that share the same colour features with the target object are very close to one another, resulting these objects are in the same search window. Therefore, this study proposed the embedment of two binary classifiers trained by SVM into the existing CAMshift. These classifiers were modeled to verify the back-projected distribution under 4 types of representations and to distinguish target and non target objects. The aim is to maintain the search window to cover only the target object during tracking. Experiments were conducted to verify the performance of the classifier under three environments namely easy, adjacent and cluttered. Results have shown that the classifier has managed to classify true detection with up to 80%.

Keywords

CAMshift Support Vector Machine object tracking 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Nur Ariffin Mohd Zin
    • 1
  • Siti Norul Huda Sheikh Abdullah
    • 2
  • Azizi Abdullah
    • 2
  1. 1.Faculty of Computer Science and Information TechnologyUniversiti Tun Hussein Onn MalaysiaParit RajaMalaysia
  2. 2.Center for Artificial Intelligence Technology, Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiMalaysia

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