Object tracking based on an online learning network with total error rate minimization
作者：
Highlights：
• An online learning network is integrated into particle filtering.
• The number of particles and its spread range are automatically adjusted.
• A sampling technique is proposed using discriminative and generative confidence.
• An automatic updating scheme is proposed for self-adaptation.
• An extensive comparison with state-of-art methods.
摘要
Highlights•An online learning network is integrated into particle filtering.•The number of particles and its spread range are automatically adjusted.•A sampling technique is proposed using discriminative and generative confidence.•An automatic updating scheme is proposed for self-adaptation.•An extensive comparison with state-of-art methods.
论文关键词：Object tracking,Particle filter,Self-adaptation,Random projection network,Online learning
论文评审过程：Received 9 November 2012, Revised 12 March 2014, Accepted 25 July 2014, Available online 12 August 2014.
论文官网地址：https://doi.org/10.1016/j.patcog.2014.07.020
