Manipulative Action Recognition for Human-Robot Interaction

Li, Zhe; Wachsmuth, Sven; Fritsch, Jannik; Sagerer, Gerhard

doi:10.5772/4958

Cited by 3 publications

(3 citation statements)

References 29 publications

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“…Theodoridis et al [35] used activity recognition in robotic systems to discern aggressive activities in humans. Li et al [17] discuss the importance of non-verbal communication between human and robot and developed a method to recognize simple activities that are nondeterministic in nature, while other works have focused on developing robots that utilizes activity recognition to imitate human activities [5,20]. However, we are more interested here in assistive robots.…”

Section: Introductionmentioning

confidence: 99%

Unstructured human activity detection from RGBD images

Sung

Ponce

Selman

et al. 2012

2012 IEEE International Conference on Robotics and Automation

196

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Abstract-Being able to detect and recognize human activities is essential for several applications, including personal assistive robotics. In this paper, we perform detection and recognition of unstructured human activity in unstructured environments. We use a RGBD sensor (Microsoft Kinect) as the input sensor, and compute a set of features based on human pose and motion, as well as based on image and pointcloud information. Our algorithm is based on a hierarchical maximum entropy Markov model (MEMM), which considers a person's activity as composed of a set of sub-activities. We infer the two-layered graph structure using a dynamic programming approach. We test our algorithm on detecting and recognizing twelve different activities performed by four people in different environments, such as a kitchen, a living room, an office, etc., and achieve good performance even when the person was not seen before in the training set. 1

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Section: Introductionmentioning

confidence: 99%

Unstructured human activity detection from RGBD images

Sung

Ponce

Selman

et al. 2012

2012 IEEE International Conference on Robotics and Automation

196

View full text Add to dashboard Cite

show abstract

“…In order to reduce the complexity of learning, Li et al [8] use a two-layered approach inspired by speech recognition. For every relevant object, a set of motion primitives, whose features are also relative to the object, is kept on the bottom layer.…”

Section: Related Workmentioning

confidence: 99%

Incremental action recognition and generalizing motion generation based on goal-directed features

Gräve

Behnke

2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-The ability to recognize human actions is a fundamental problem in many areas of robotics research concerned with human-robot interaction or learning from human demonstration. In this paper, we present a new integrated approach to identifying and recognizing actions in human movement sequences and their reproduction in unknown situations. We propose a set of task-space features to construct probabilistic models of action classes. Based on this representation, we suggest a combined segmentation and classification algorithm which processes data non-greedily using an incremental lookahead to reliably locate transitions between actions. In a programming by demonstration scenario, our action models afford the generalization and reproduction of learned movements to previously unseen situations. To evaluate the performance of our approach, we consider typical manipulation tasks in a table top setting. In a sequence of human demonstrations, our approach successfully extracts and recognizes actions from different classes and subsequently generalizes them to unknown situations.

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“…The wide range of important applications that rely on image 3.1. Spectral Image Segmentation Methods segmentation such as image coding [Jiang et al, 2013, Wu, 1992, content-based image retrieval [Bruno et al, 2008, Ozden andPolat, 2007] and object recognition [Li et al, 2007, Tu et al, 2005, have motivated the development of an enormous quantity of techniques for segmenting images. In particular, graph-based techniques figure among the most effective methods, mainly due to the flexibility it provides to handle specific attributes of the image [Diaz et al, 2002, Falcão et al, 2004, Noma et al, 2012, Pratt, 2008, Shi and Malik, 2000.…”

Section: Spectral Image Segmentation Methodsmentioning

confidence: 99%

Graph Laplacian for spectral clustering and seeded image segmentation

Casaca¹,

Taubin²,

Nonato³

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I mage segmentation is an essential tool to enhance the ability of computer systems to efficiently perform elementary cognitive tasks such as detection, recognition and tracking. In this thesis we concentrate on the investigation of two fundamental topics in the context of image segmentation: spectral clustering and seeded image segmentation. We introduce two new algorithms for those topics that, in summary, rely on Laplacian-based operators, spectral graph theory, and minimization of energy functionals. The effectiveness of both segmentation algorithms is verified by visually evaluating the resulting partitions against state-of-the-art methods as well as through a variety of quantitative measures typically employed as benchmark by the image segmentation community. Our spectral-based segmentation algorithm combines image decomposition, similarity metrics, and spectral graph theory into a concise and powerful framework. An image decomposition is performed to split the input image into texture and cartoon components. Then, an affinity graph is generated and weights are assigned to the edges of the graph according to a gradient-based inner-product function. From the eigenstructure of the affinity graph, the image is partitioned through the spectral cut of the underlying graph. Moreover, the image partitioning can be improved by changing the graph weights by sketching interactively. Visual and numerical evaluation were conducted against representative spectral-based segmentation techniques using boundary and partition quality measures in the well-known BSDS dataset. vii Unlike most existing seed-based methods that rely on complex mathematical formulations that typically do not guarantee unique solution for the segmentation problem while still being prone to be trapped in local minima, our segmentation approach is mathematically simple to formulate, easy-to-implement, and it guarantees to produce a unique solution. Moreover, the formulation holds an anisotropic behavior, that is, pixels sharing similar attributes are preserved closer to each other while big discontinuities are naturally imposed on the boundary between image regions, thus ensuring better fitting on object boundaries. We show that the proposed approach significantly outperforms competing techniques both quantitatively as well as qualitatively, using the classical "GrabCut" dataset from Microsoft as a benchmark. While most of this research concentrates on the particular problem of segmenting an image, we also develop two new techniques to address the problem of image inpainting and photo colorization. Both methods couple the developed segmentation tools with other computer vision approaches in order to operate properly.

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Manipulative Action Recognition for Human-Robot Interaction

Cited by 3 publications

References 29 publications

Unstructured human activity detection from RGBD images

Unstructured human activity detection from RGBD images

Incremental action recognition and generalizing motion generation based on goal-directed features

Graph Laplacian for spectral clustering and seeded image segmentation

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