HON4D: Histogram of Oriented 4D Normals for Activity Recognition from Depth Sequences
We present a new descriptor for activity recognition from videos acquired by a depth sensor. Previous descriptors mostly compute shape and motion features independently, thus, they often fail to capture the complex joint shape-motion cues at pixel-level. In contrast, we describe the depth sequence u...
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| Veröffentlicht in: | 2013 IEEE Conference on Computer Vision and Pattern Recognition S. 716 - 723 |
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| Sprache: | Englisch |
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IEEE
01.06.2013
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| ISSN: | 1063-6919, 1063-6919 |
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| Abstract | We present a new descriptor for activity recognition from videos acquired by a depth sensor. Previous descriptors mostly compute shape and motion features independently, thus, they often fail to capture the complex joint shape-motion cues at pixel-level. In contrast, we describe the depth sequence using a histogram capturing the distribution of the surface normal orientation in the 4D space of time, depth, and spatial coordinates. To build the histogram, we create 4D projectors, which quantize the 4D space and represent the possible directions for the 4D normal. We initialize the projectors using the vertices of a regular polychoron. Consequently, we refine the projectors using a discriminative density measure, such that additional projectors are induced in the directions where the 4D normals are more dense and discriminative. Through extensive experiments, we demonstrate that our descriptor better captures the joint shape-motion cues in the depth sequence, and thus outperforms the state-of-the-art on all relevant benchmarks. |
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| AbstractList | We present a new descriptor for activity recognition from videos acquired by a depth sensor. Previous descriptors mostly compute shape and motion features independently, thus, they often fail to capture the complex joint shape-motion cues at pixel-level. In contrast, we describe the depth sequence using a histogram capturing the distribution of the surface normal orientation in the 4D space of time, depth, and spatial coordinates. To build the histogram, we create 4D projectors, which quantize the 4D space and represent the possible directions for the 4D normal. We initialize the projectors using the vertices of a regular polychoron. Consequently, we refine the projectors using a discriminative density measure, such that additional projectors are induced in the directions where the 4D normals are more dense and discriminative. Through extensive experiments, we demonstrate that our descriptor better captures the joint shape-motion cues in the depth sequence, and thus outperforms the state-of-the-art on all relevant benchmarks. |
| Author | Oreifej, Omar Zicheng Liu |
| Author_xml | – sequence: 1 givenname: Omar surname: Oreifej fullname: Oreifej, Omar email: oreifej@eecs.ucf.edu organization: Univ. of Central Florida, Orlando, FL, USA – sequence: 2 surname: Zicheng Liu fullname: Zicheng Liu email: zliu@microsoft.edu organization: Microsoft Res., Redmond, WA, USA |
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| Snippet | We present a new descriptor for activity recognition from videos acquired by a depth sensor. Previous descriptors mostly compute shape and motion features... |
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| SubjectTerms | 4D Normals Action Recognition Activity Recognition Depth Histogram of Gradients Histogram of Normals Histograms HOG HON Joints Kinect MSR Action 3D MSR Action Pairs MSR Daily Activity Polychoron Quantization (signal) Shape Support vector machines Three-dimensional displays Vectors |
| Title | HON4D: Histogram of Oriented 4D Normals for Activity Recognition from Depth Sequences |
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