• Corpus ID: 9030123

Enhancing Probabilistic Appearance-Based Object Tracking with Depth Information : Object Tracking under Occlusion (情報論的学習理論と機械学習)

@article{Meshgi2013EnhancingPA,
  title={Enhancing Probabilistic Appearance-Based Object Tracking with Depth Information : Object Tracking under Occlusion (情報論的学習理論と機械学習)},
  author={Kourosh Meshgi and Yu-zhe Li and Shigeyuki Oba and Shin-ichi Maeda and Shin Ishii},
  journal={IEICE technical report. Speech},
  year={2013},
  volume={113},
  pages={85-91}
}
†Graduate School of Informatics, Kyoto University, G okasho, Uji-shi, Kyoto, 611-0011 Japan E-mail: †{meshgi-k,li-yuzhe,oba,ichi,ishii}@sys.i.kyoto-u.ac. jp Abstract Object tracking has attracted recent attention bec ause of high demands for its everyday-life applicat ions. Handling occlusions especially in cluttered environments introduced new challenges to the tracking problem; identity loss, splitting/merging, shape changes, shadows and other appearance artifacts trouble appearance-based… 

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