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多示例深度学习目标跟踪

程帅 孙俊喜 曹永刚 刘广文 韩广良

程帅, 孙俊喜, 曹永刚, 刘广文, 韩广良. 多示例深度学习目标跟踪[J]. 电子与信息学报, 2015, 37(12): 2906-2912. doi: 10.11999/JEIT150319
引用本文: 程帅, 孙俊喜, 曹永刚, 刘广文, 韩广良. 多示例深度学习目标跟踪[J]. 电子与信息学报, 2015, 37(12): 2906-2912. doi: 10.11999/JEIT150319
Cheng Shuai, Sun Jun-xi, Cao Yong-gang, Liu Guang-wen, Hang Guang-liang. Target Tracking Based on Multiple Instance Deep Learning[J]. Journal of Electronics & Information Technology, 2015, 37(12): 2906-2912. doi: 10.11999/JEIT150319
Citation: Cheng Shuai, Sun Jun-xi, Cao Yong-gang, Liu Guang-wen, Hang Guang-liang. Target Tracking Based on Multiple Instance Deep Learning[J]. Journal of Electronics & Information Technology, 2015, 37(12): 2906-2912. doi: 10.11999/JEIT150319

多示例深度学习目标跟踪

doi: 10.11999/JEIT150319
基金项目: 

国家自然科学基金(61172111)和吉林省科技厅项目(20090512, 20100312)

Target Tracking Based on Multiple Instance Deep Learning

Funds: 

The National Natural Science Foundation of China (61172111)

  • 摘要: 为解决多示例跟踪算法中外观模型和运动模型不足导致跟踪精度不高的问题,该文提出多示例深度学习目标跟踪算法。针对原始多示例跟踪算法中采用Haar-like特征不能有效表达图像信息的缺点,利用深度去噪自编码器提取示例图像的有效特征,实现图像信息的本质表达,易于分类器正确分类,提高跟踪精度。针对多示例学习跟踪算法中选取弱特征向量不能更换,难以反映目标自身和外界条件变化的缺点,在选择弱分类器过程中,实时替换判别力最弱的特征以适应目标外观的变化。针对原始多示例跟踪算法中运动模型中仅假设帧间物体运动不会超过某个范围,不能有效反映目标的运动状态的缺点,引入粒子滤波算法对目标进行预测,提高跟踪的准确性。在复杂环境下不同图片序列实验结果表明,与多示例跟踪算法及其他跟踪算法相比,该文算法具有更高跟踪精确度和更好的鲁棒性。
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出版历程
  • 收稿日期:  2015-03-17
  • 修回日期:  2015-07-27
  • 刊出日期:  2015-12-19

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