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A hybrid motion and appearance prediction model for robust visual object tracking

机译:鲁棒的视觉对象跟踪的混合运动和外观预测模型

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摘要

In this paper a new video object tracking method is proposed. A hybrid model based on motion and appearance is constructed for the object and Kalman filter is applied to both components in order to reduce noise and provide a prediction for the next frame. Making available a prediction of the object appearance in the next frame contributes effectively in robust object tracking in spite of large changes in scene illumination. Experimental results using the proposed method and its counterparts without appearance prediction demonstrate the superiority of the novel hybrid prediction method under drastic changes in illumination.
机译:本文提出了一种新的视频目标跟踪方法。为对象构建基于运动和外观的混合模型,并将卡尔曼滤波器应用于这两个组件,以减少噪声并为下一帧提供预测。尽管场景照度发生了很大变化,但在下一帧中提供对对象外观的预测仍然可以有效地进行可靠的对象跟踪。使用所提出的方法及其对应的没有外观预测的实验结果证明了在光照剧烈变化下新型混合预测方法的优越性。

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