Robust Visual Tracking Using Oblique Random Forests

CVPR 2017 Le ZhangJagannadan VaradarajanPonnuthurai Nagaratnam SuganthanNarendra AhujaPierre Moulin

Random forest has emerged as a powerful classification technique with promising results in various vision tasks including image classification, pose estimation and object detection. However, current techniques have shown little improvements in visual tracking as they mostly rely on piece wise orthogonal hyperplanes to create decision nodes and lack a robust incremental learning mechanism that is much needed for online tracking... (read more)

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