Online Learning and Detection with Part-based Circulant Structure

Akin O., Mikolajczyk K.

22nd International Conference on Pattern Recognition (ICPR), Stockholm, İsveç, 24 - 28 Ağustos 2014, ss.4229-4233 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Doi Numarası: 10.1109/icpr.2014.725
  • Basıldığı Şehir: Stockholm
  • Basıldığı Ülke: İsveç
  • Sayfa Sayıları: ss.4229-4233
  • Hacettepe Üniversitesi Adresli: Evet

Özet

Circulant Structure Kernel (CSK) has recently been introduced as a simple and extremely efficient tracking method. In this paper, we propose an extension of CSK that explicitly addresses partial occlusion problems which the original CSK suffers from. Our extension is based on a part-based scheme, which improves the robustness and localisation accuracy. Furthermore, we improve the robustness of CSK for long-term tracking by incorporating it into an online learning and detection framework. We provide an extensive comparison to eight recently introduced tracking methods. Our experimental results show that the proposed approach significantly improves the original CSK and provides state-of-the-art results when combined with online learning approach.