关键词: 均值Hash, 多实例学习, 在线学习, 目标检测与跟踪, 分类器

Abstract: Target tracking in complex scenes has been a challenging issue facing the research communities worldwide. In this paper, a random local mean-hash feature based tracking method is proposed. The core of the proposed approach lies with the target modeling using Poisson probability distribution and model updating by online adaptive learning. The presented algorithm is put to test against other four existing learning-based standard tracking algorithms, that is, OnlineBoostingTracker, SemiTracker, BeyondSemiTracker, Context Tracker and MILTracker. The results indicate that the presented algorithm achieves better comprehensive tracking performances, particularly in occlusion resistance. The improvement is needed in the algorithm’s anti-rotation performance which will be the focus in the feature study.

Key words: mean-Hash, multiple instance learning, online learning, object detection and tracking, classifier