Analysis of Motion Self-Occlusion Problem Due to Motion Overwriting for Human Activity Recognition
Analysis of Motion Self-Occlusion Problem Due to Motion Overwriting for Human Activity Recognition
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DOI：
10.4304/jmm.5.1.36-46
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发表时间：
2010-01
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影响因子：
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通讯作者：
Md Atiqur Rahman Ahad;J. Tan;Hyoungseop Kim;S. Ishikawa
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文献类型：
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作者：
Md Atiqur Rahman Ahad;J. Tan;Hyoungseop Kim;S. Ishikawa
各种识别方法致力于识别和理解各种人类活动。然而，由于同一区域的运动重叠而导致的运动自遮挡是一个令人望而生畏的任务。各种运动识别方法要么绕过这个问题，要么以复杂的方式解决这个问题。基于外观的模板匹配范例更简单，因此这些方法更快地进行活动分析。在本文中，我们主要研究在各种复杂的识别活动中，由于运动重叠而产生的运动自遮挡问题。在运动历史图像(MHI)方法中，自遮挡现象十分明显，亟待解决。因此，本文将我们的定向运动历史图像概念与基本运动历史图像、多层次运动历史图像表示和分层运动历史直方图表示进行比较，以解决基本运动历史图像表示的自遮挡问题。我们使用了一些复杂的有氧运动，并发现与其他方法相比，我们的方法对于这个自遮挡问题具有更好的稳健性。我们使用7个高阶Hu矩来计算每个活动的特征向量。然后，利用k-最近邻法进行留一范式的分类。比较结果清楚地表明了我们的方法比最近其他方法的优越性。我们还给出了几个实验，以证明DMHI方法在识别各种复杂动作方面的性能和优势。索引项-MHI、DMHI、MMHI、HMHH、运动识别、特征向量
Various recognition methodologies address to recognize and understand varieties of human activities. However, motion self-occlusion due to motion overlapping in the same region is a daunting task to solve. Various motion-recognition methods either bypass this problem or solve this problem in complex manner. Appearance-based template matching paradigms are simpler and hence these approaches faster for activity analysis. In this paper, we concentrate on motion self- occlusion problem due to motion overlapping in various complex activities for recognition. In the Motion History Image (MHI) method, the self-occlusion is evident and it should be solved. Therefore, this paper compares our directional motion history image concept with basic the Motion History Image, Multi-level Motion History representation and Hierarchical Motion History Histogram representation to solve the self-occlusion problem of basic the Motion History Image representation. We employ some complex aerobics and find the robustness of our method compared to other methods for this self-occlusion problem. We employ seven higher order Hu moments to compute the feature vector for each activity. Afterwards, k-nearest neighbor method is utilized for classification with leave-one-out paradigm. The comparative results clearly demonstrate the superiority of our method than other recent approaches. We also present several experiments to demonstrate the performance and strength of the DMHI method in recognizing various complex actions. Index Terms—MHI, DMHI, MMHI, HMHH, motion recognition, feature vector