کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6939309 1449970 2018 35 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Tensor-based linear dynamical systems for action recognition from 3D skeletons
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
Tensor-based linear dynamical systems for action recognition from 3D skeletons
چکیده انگلیسی
Recent years have seen a growth in interest in skeleton-based human behavior recognition. Skeleton sequences can be expressed naturally as high-order tensor time series, and in this paper we report on the modeling and analysis of such time series using a linear dynamical system (LDS). Owing to their relative simplicity and efficiency, LDSs are the most common tool used in various disciplines for encoding spatiotemporal time series data. However, conventional LDSs process the latent and observed states at each frame of a video as a column vector, a representation that fails to take into account valuable structural information associated with human action. To correct this, we propose a tensor-based linear dynamical system (tLDS) for modeling tensor observations in time series and employ Tucker decomposition to estimate the parameters of the LDS model as action descriptors. In this manner, an action can be expressed as a subspace corresponding to a point on a Grassmann manifold on which classification can be performed using dictionary learning and sparse coding. Experiments using the MSR Action3D, UCF Kinect, and Northwestern-UCLA Multiview Action3D datasets demonstrate the excellent performance of our proposed method.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 77, May 2018, Pages 75-86
نویسندگان
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