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Integrating Local Binary Patterns into Normalized Moment of Inertia for Updating Tracking Templates

机译:将局部二进制模式整合到惯性的标准化矩中以更新跟踪模板

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摘要

This paper presents an efficient visual tracking framework which is robust to rotation, scale variation and occlusion. The target template is characterized by Local binary patterns (LBP), which exhibit invariance to rotation. The LBP features are then integrated into the Normalized moment of inertia (NMI) to decide whether the template requires update. This procedure enables an adaptive template matching strategy which addresses the tracking failures arising from scale variations. Kalman filtering is exploited for predicting the trajectory of the target when it is occluded. The matching efficiency is achieved by applying a locally pyramid searching scheme. Experimental results validate the efficiency and effectiveness of our tracking framework.
机译:本文提出了一种有效的视觉跟踪框架,该框架对旋转,缩放比例变化和遮挡具有鲁棒性。目标模板的特征在于局部二进制模式(LBP),该模式对旋转具有不变性。然后将LBP功能集成到归一化惯性矩(NMI)中,以确定模板是否需要更新。此过程启用了自适应模板匹配策略,该策略可以解决因比例变化而引起的跟踪失败。卡尔曼滤波被用于预测目标被遮挡时的轨迹。通过应用局部金字塔搜索方案可以实现匹配效率。实验结果验证了我们跟踪框架的效率和有效性。

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