暂无分享,去创建一个
[1] Mario Fernando Montenegro Campos,et al. STOP: Space-Time Occupancy Patterns for 3D Action Recognition from Depth Map Sequences , 2012, CIARP.
[2] Z. Liu,et al. A real time system for dynamic hand gesture recognition with a depth sensor , 2012, 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO).
[3] Nasser Kehtarnavaz,et al. Action Recognition from Depth Sequences Using Depth Motion Maps-Based Local Binary Patterns , 2015, 2015 IEEE Winter Conference on Applications of Computer Vision.
[4] Juan Carlos Niebles,et al. Discriminative Hierarchical Modeling of Spatio-temporally Composable Human Activities , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[5] Alois Knoll,et al. Action recognition using ensemble weighted multi-instance learning , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).
[6] Alois Knoll,et al. Combining unsupervised learning and discrimination for 3D action recognition , 2015, Signal Process..
[7] Nasser Kehtarnavaz,et al. Real-time human action recognition based on depth motion maps , 2016, Journal of Real-Time Image Processing.
[8] Mathieu Barnachon,et al. Ongoing human action recognition with motion capture , 2014, Pattern Recognit..
[9] Andreas E. Savakis,et al. 3D Action Classification Using Sparse Spatio-temporal Feature Representations , 2012, ISVC.
[10] Ngoc Q. Ly,et al. An effective fusion scheme of spatio-temporal features for human action recognition in RGB-D video , 2013, 2013 International Conference on Control, Automation and Information Sciences (ICCAIS).
[11] Quan Z. Sheng,et al. Online human gesture recognition from motion data streams , 2013, ACM Multimedia.
[12] Jing Zhang,et al. Deep Convolutional Neural Networks for Action Recognition Using Depth Map Sequences , 2015, ArXiv.
[13] Rui Yang,et al. DMM-Pyramid Based Deep Architectures for Action Recognition with Depth Cameras , 2014, ACCV.
[14] Ruzena Bajcsy,et al. Bio-inspired Dynamic 3D Discriminative Skeletal Features for Human Action Recognition , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops.
[15] Arif Mahmood,et al. Real time action recognition using histograms of depth gradients and random decision forests , 2014, IEEE Winter Conference on Applications of Computer Vision.
[16] Bart Selman,et al. Unstructured human activity detection from RGBD images , 2011, 2012 IEEE International Conference on Robotics and Automation.
[17] Hema Swetha Koppula,et al. Learning human activities and object affordances from RGB-D videos , 2012, Int. J. Robotics Res..
[18] Alexandros André Chaaraoui,et al. Evolutionary joint selection to improve human action recognition with RGB-D devices , 2014, Expert Syst. Appl..
[19] Christian Wolf,et al. Fast Exact Hyper-graph Matching with Dynamic Programming for Spatio-temporal Data , 2014, Journal of Mathematical Imaging and Vision.
[20] Jake K. Aggarwal,et al. Spatio-temporal Depth Cuboid Similarity Feature for Activity Recognition Using Depth Camera , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[21] Yiannis Demiris,et al. Iterative temporal learning and prediction with the sparse online echo state gaussian process , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).
[22] Zicheng Liu,et al. HON4D: Histogram of Oriented 4D Normals for Activity Recognition from Depth Sequences , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[23] Gioia Ballin,et al. 3D Flow Estimation for Human Action Recognition from Colored Point Clouds , 2013, BICA 2013.
[24] Xiaodong Yang,et al. Super Normal Vector for Activity Recognition Using Depth Sequences , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[25] Alan L. Yuille,et al. An Approach to Pose-Based Action Recognition , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[26] Qi Tian,et al. Human Daily Action Analysis with Multi-view and Color-Depth Data , 2012, ECCV Workshops.
[27] Mario Fernando Montenegro Campos,et al. Online gesture recognition from pose kernel learning and decision forests , 2014, Pattern Recognit. Lett..
[28] Quan Z. Sheng,et al. Effective approaches in human action recognition , 2013, 2013 International Conference on Advanced Computer Science and Information Systems (ICACSIS).
[29] Hugo Jair Escalante,et al. A One-Shot DTW-Based Method for Early Gesture Recognition , 2013, CIARP.
[30] Mohammad H. Mahoor,et al. Human activity recognition using multi-features and multiple kernel learning , 2014, Pattern Recognit..
[31] Alexandros André Chaaraoui,et al. Optimal Joint Selection for Skeletal Data from RGB-D Devices Using a Genetic Algorithm , 2012, MICAI.
[32] Jake K. Aggarwal,et al. View invariant human action recognition using histograms of 3D joints , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.
[33] Ying Wu,et al. Learning Maximum Margin Temporal Warping for Action Recognition , 2013, 2013 IEEE International Conference on Computer Vision.
[34] Ngoc Quoc Ly,et al. Sparse spatio-temporal representation of joint shape-motion cues for human action recognition in depth sequences , 2013, The 2013 RIVF International Conference on Computing & Communication Technologies - Research, Innovation, and Vision for Future (RIVF).
[35] Christian Bauckhage,et al. Efficient Pose-Based Action Recognition , 2014, ACCV.
[36] Ying Wu,et al. Human Action Recognition with Depth Cameras , 2014, SpringerBriefs in Computer Science.
[37] Georgios Evangelidis,et al. Skeletal Quads: Human Action Recognition Using Joint Quadruples , 2014, 2014 22nd International Conference on Pattern Recognition.
[38] Arif Mahmood,et al. Action Classification with Locality-Constrained Linear Coding , 2014, 2014 22nd International Conference on Pattern Recognition.
[39] Joseph J. LaViola,et al. Exploring the Trade-off Between Accuracy and Observational Latency in Action Recognition , 2013, International Journal of Computer Vision.
[40] Mario Fernando Montenegro Campos,et al. Real-Time Gesture Recognition from Depth Data through Key Poses Learning and Decision Forests , 2012, 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images.
[41] Ying Wu,et al. Mining actionlet ensemble for action recognition with depth cameras , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[42] Mohan M. Trivedi,et al. Joint Angles Similarities and HOG2 for Action Recognition , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops.
[43] Ying Wu,et al. Robust 3D Action Recognition with Random Occupancy Patterns , 2012, ECCV.
[44] Christophe Garcia,et al. Human activities dataset and the ICPR 2012 human activities recognition and localization competition , 2012 .
[45] Arif Mahmood,et al. HOPC: Histogram of Oriented Principal Components of 3D Pointclouds for Action Recognition , 2014, ECCV.
[46] Alberto Del Bimbo,et al. Recognizing Actions from Depth Cameras as Weakly Aligned Multi-part Bag-of-Poses , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops.
[47] Shuxin Qin,et al. Gesture recognition from depth images using motion and shape features , 2013, 2013 2nd International Symposium on Instrumentation and Measurement, Sensor Network and Automation (IMSNA).
[48] Dimitris Kastaniotis,et al. Pose-based human action recognition via sparse representation in dissimilarity space , 2014, J. Vis. Commun. Image Represent..
[49] Shuicheng Yan,et al. Body Surface Context: A New Robust Feature for Action Recognition From Depth Videos , 2014, IEEE Transactions on Circuits and Systems for Video Technology.
[50] Wanqing Li,et al. Action recognition based on a bag of 3D points , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.
[51] Yanbing Xue,et al. Human Action Recognition Via Multi-modality Information , 2014 .
[52] Jun Yu,et al. Machine learning and signal processing for human pose recovery and behavior analysis , 2015, Signal Process..
[53] Junsong Yuan,et al. Learning Actionlet Ensemble for 3D Human Action Recognition , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[54] Lihong Zheng,et al. Three Dimensional Motion Trail Model for Gesture Recognition , 2013, 2013 IEEE International Conference on Computer Vision Workshops.
[55] Guangping Xu,et al. Human Behavior Recognition Based on Axonometric Projections and PHOG Feature , 2014 .
[56] Marco Morana,et al. Motion sensors for activity recognition in an ambient-intelligence scenario , 2013, 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).
[57] Helena M. Mentis,et al. Instructing people for training gestural interactive systems , 2012, CHI.
[58] Alexandros André Chaaraoui,et al. Fusion of Skeletal and Silhouette-Based Features for Human Action Recognition with RGB-D Devices , 2013, 2013 IEEE International Conference on Computer Vision Workshops.
[59] Nikos Nikolaidis,et al. Action recognition on motion capture data using a dynemes and forward differences representation , 2014, J. Vis. Commun. Image Represent..
[60] Peter Carr,et al. Hybrid robotic/virtual pan-tilt-zom cameras for autonomous event recording , 2013, ACM Multimedia.
[61] Pavan K. Turaga,et al. Attractor-Shape for Dynamical Analysis of Human Movement: Applications in Stroke Rehabilitation and Action Recognition , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops.
[62] Marwan Torki,et al. Histogram of Oriented Displacements (HOD): Describing Trajectories of Human Joints for Action Recognition , 2013, IJCAI.
[63] Aytül Erçil,et al. A Decision Forest Based Feature Selection Framework for Action Recognition from RGB-Depth Cameras , 2013, ICIAR.
[64] Dimitrios Makris,et al. G3D: A gaming action dataset and real time action recognition evaluation framework , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.
[65] Ruzena Bajcsy,et al. Berkeley MHAD: A comprehensive Multimodal Human Action Database , 2013, 2013 IEEE Workshop on Applications of Computer Vision (WACV).
[66] Zicheng Liu,et al. Random Occupancy Patterns , 2014 .
[67] Bingbing Ni,et al. RGBD-HuDaAct: A color-depth video database for human daily activity recognition , 2011, ICCV Workshops.
[68] Alberto Del Bimbo,et al. Space-Time Pose Representation for 3D Human Action Recognition , 2013, ICIAP Workshops.
[69] Rui Zhang,et al. Human Action Recognition by Mining Discriminative Segment with Novel Skeleton Joint Feature , 2013, PCM.
[70] Gérard G. Medioni,et al. Home Monitoring Musculo-skeletal Disorders with a Single 3D Sensor , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops.
[71] Hairong Qi,et al. Spatio-temporal feature extraction and representation for RGB-D human action recognition , 2014, Pattern Recognit. Lett..
[72] Emanuele Frontoni,et al. Customers’ activity recognition in intelligent retail environments , 2013 .
[73] Bülent Sankur,et al. Graph-based analysis of physical exercise actions , 2013, MIIRH '13.
[74] Jun Kong,et al. Informative joints based human action recognition using skeleton contexts , 2015, Signal Process. Image Commun..
[75] Cristian Sminchisescu,et al. The Moving Pose: An Efficient 3D Kinematics Descriptor for Low-Latency Action Recognition and Detection , 2013, 2013 IEEE International Conference on Computer Vision.
[76] Ruzena Bajcsy,et al. Sequence of the Most Informative Joints (SMIJ): A new representation for human skeletal action recognition , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.
[77] Andreas E. Savakis,et al. Grassmannian Sparse Representations and Motion Depth Surfaces for 3D Action Recognition , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops.
[78] Xiaodong Yang,et al. Effective 3D action recognition using EigenJoints , 2014, J. Vis. Commun. Image Represent..
[79] Guodong Guo,et al. Fusing Spatiotemporal Features and Joints for 3D Action Recognition , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops.
[80] Marwan Torki,et al. Human Action Recognition Using a Temporal Hierarchy of Covariance Descriptors on 3D Joint Locations , 2013, IJCAI.
[81] Mario Fernando Montenegro Campos,et al. On the improvement of human action recognition from depth map sequences using Space-Time Occupancy Patterns , 2014, Pattern Recognit. Lett..
[82] Hairong Qi,et al. Group Sparsity and Geometry Constrained Dictionary Learning for Action Recognition from Depth Maps , 2013, 2013 IEEE International Conference on Computer Vision.