基于稀疏编码和推土机距离的视觉跟踪

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摘要翻译：
提出了一种高效的迭代地球移动距离(iEMD)视觉跟踪算法。利用推土机距离(EMD)作为相似性度量，在视频序列的特征空间中搜索最优模板候选。EMD的计算由线性规划化为运输问题。EMD优化问题的效率限制了其在视觉跟踪中的应用。为了解决这一问题，将运输单纯形法用于EMD优化，并提出了一种单调收敛的迭代优化算法。局部稀疏表示被用作iEMD跟踪器的外观模型。采用最大对齐池方法构造稀疏编码直方图，降低了EMD优化的计算复杂度。提出了基于EMD的模板更新算法。为了保证收敛性，iEMD跟踪算法假设帧间移动较小。当摄像机安装在移动机器人上，例如飞行的四分机上时，摄像机会经历突然而快速的运动，导致帧间的大运动。为了保证跟踪算法的收敛性，提出了一种陀螺辅助的iEMD跟踪器扩展，利用同步的陀螺信息来补偿摄像机的旋转。使用八个公开的数据集对iEMD算法的性能进行了评估。将iEMD算法与现有的七种基于相对百分比重叠的跟踪算法进行了性能比较。文中还说明了该算法对大帧间位移的鲁棒性。
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英文标题：
《Visual Tracking Using Sparse Coding and Earth Mover's Distance》
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作者：
Gang Yao, Ashwin Dani
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Robotics        机器人学
分类描述：Roughly includes material in ACM Subject Class I.2.9.
大致包括ACM科目I.2.9类的材料。
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英文摘要：
  An efficient iterative Earth Mover's Distance (iEMD) algorithm for visual tracking is proposed in this paper. The Earth Mover's Distance (EMD) is used as the similarity measure to search for the optimal template candidates in feature-spatial space in a video sequence. The computation of the EMD is formulated as the transportation problem from linear programming. The efficiency of the EMD optimization problem limits its use for visual tracking. To alleviate this problem, a transportation-simplex method is used for EMD optimization and a monotonically convergent iterative optimization algorithm is developed. The local sparse representation is used as the appearance models for the iEMD tracker. The maximum-alignment-pooling method is used for constructing a sparse coding histogram which reduces the computational complexity of the EMD optimization. The template update algorithm based on the EMD is also presented. The iEMD tracking algorithm assumes small inter-frame movement in order to guarantee convergence. When the camera is mounted on a moving robot, e.g., a flying quadcopter, the camera could experience a sudden and rapid motion leading to large inter-frame movements. To ensure that the tracking algorithm converges, a gyro-aided extension of the iEMD tracker is presented, where synchronized gyroscope information is utilized to compensate for the rotation of the camera. The iEMD algorithm's performance is evaluated using eight publicly available datasets. The performance of the iEMD algorithm is compared with seven state-of-the-art tracking algorithms based on relative percentage overlap. The robustness of this algorithm for large inter-frame displacements is also illustrated.
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PDF链接：
https://arxiv.org/pdf/1804.0247

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关键词：推土机 Optimization Presentation Displacement Constructing 摄像机 收敛性 距离 陀螺 Distance