机译:在线多实例渐变特征选择,实现强大的视觉跟踪
State Key Lab. of Intelligent Control and Management of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;
Video and Image Lab., Department of Computer Science, Xiamen University, Xiamen 361005, China;
Video and Image Lab., Department of Computer Science, Xiamen University, Xiamen 361005, China;
State Key Lab. of Intelligent Control and Management of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;
gradient-based feature selection; HOG; multiple instance learning; online object tracking;
机译:通过在线多实例学习和Fisher信息进行可靠的视觉跟踪
机译:通过最小化跟踪,对象跟踪在线功能选择多实例学习
机译:通过在线信息功能选择进行可靠的视觉跟踪
机译:通过基于SIFT功能的在线多实例学习算法进行可靠的视觉跟踪
机译:使用Reid特征和图形卷积网络强大的多个对象跟踪
机译:基于改进的在线多实例学习算法的视觉跟踪
机译:实例意义引导多实例提升鲁棒性 视觉跟踪