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首页> 外文期刊>Multimedia Tools and Applications >A novel unsupervised 3D skeleton detection in RGB-D images for video surveillance
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A novel unsupervised 3D skeleton detection in RGB-D images for video surveillance

机译：RGB-D图像中的一种新型无监督的3D骨架检测，用于视频监控

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In this paper we present a novel moment-based skeleton detection for representing human objects in RGB-D videos with animated 3D skeletons. An object often consists of several parts, where each of them can be concisely represented with a skeleton. However, it remains as a challenge to detect the skeletons of individual objects in an image since it requires an effective part detector and a part merging algorithm to group parts into objects. In this paper, we present a novel fully unsupervised learning framework to detect the skeletons of human objects in a RGB-D video. The skeleton modeling algorithm uses a pipeline architecture which consists of a series of cascaded operations, i.e., symmetry patch detection, linear time search of symmetry patch pairs, part and symmetry detection, symmetry graph partitioning, and object segmentation. The properties of geometric moment-based functions for embedding symmetry features into centers of symmetry patches are also investigated in detail. As compared with the state-of-the-art deep learning approaches for skeleton detection, the proposed approach does not require tedious human labeling work on training images to locate the skeleton pixels and their associated scale information. Although our algorithm can detect parts and objects simultaneously, a pre-learned convolution neural network (CNN) can be used to locate the human object from each frame of the input video RGB-D video in order to achieve the goal of constructing real-time applications. This much reduces the complexity to detect the skeleton structure of individual human objects with our proposed method. Using the segmented human object skeleton model, a video surveillance application is constructed to verify the effectiveness of the approach. Experimental results show that the proposed method gives good performance in terms of detection and recognition using publicly available datasets.
机译：在本文中，我们介绍了一种新的基于矩的骨架检测，用于代表具有动画3D骨架的RGB-D视频中的人类对象。对象通常由几个部分组成，其中每个部分可以用骨架简明地表示。然而，检测图像中的各个对象的骨架仍然是一个挑战，因为它需要有效的零件检测器和将部分合并算法分组到对象中的零件合并算法。在本文中，我们提出了一种新颖的无监督学习框架，用于检测RGB-D视频中的人类对象的骨架。骨架建模算法使用管道架构，该架构包括一系列级联操作，即对称修补程序检测，对称性补丁对的线性时间搜索，零件和对称检测，对称性图形分区和对象分割。还详细研究了将对称特征嵌入对称性特征的几何力矩的功能。与骨架检测的最先进的深度学习方法相比，所提出的方法在训练图像上不需要繁琐的人类标记工作以定位骨架像素及其相关规范信息。虽然我们的算法可以同时检测零件和对象，但是可以使用预先学习的卷积神经网络（CNN）来定位来自输入视频RGB-D视频的每一帧的人体对象，以实现构建实时的目标应用程序。这种大大降低了通过我们提出的方法检测单个人体骨架结构的复杂性。使用分段人体对象骨架模型，构建了一种视频监控应用以验证该方法的有效性。实验结果表明，该方法在使用公共可用数据集的检测和识别方面具有良好的性能。

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