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This paper proposes a robust superpixel-based tracker via multiple-instance learning, which exploits the importance of instances and mid-level features captured by superpixels for object tracking. We first present a superpixels-based appearance model, which is able to compute the confidences of the object and background. Most importantly, we introduce the sample importance into multiple-instance learning (MIL) procedure to improve the performance of tracking. The importance for each instance indoi:10.1587/transinf.2014edl8176 fatcat:gyudiecdorduzkadxqutkrwkdu