Fusing Object Information and Inertial Data for Activity Recognition †
暂无分享,去创建一个
[1] Laurent Itti,et al. Situation awareness via sensor-equipped eyeglasses , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[2] Thomas Kirste,et al. Providing Semantic Annotation for the CMU Grand Challenge Dataset , 2018, 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops).
[3] Vijay Vasudevan,et al. Learning Transferable Architectures for Scalable Image Recognition , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[4] C. Atkeson,et al. Toward the Automatic Assessment of Behavioral Distrubances of Dementia , 2003 .
[5] Massimo Piccardi,et al. Structural SVM with Partial Ranking for Activity Segmentation and Classification , 2015, IEEE Signal Processing Letters.
[6] Pietro Perona,et al. Microsoft COCO: Common Objects in Context , 2014, ECCV.
[7] Dieter Fox,et al. Fine-grained kitchen activity recognition using RGB-D , 2012, UbiComp.
[8] Dima Damen,et al. Scaling Egocentric Vision: The EPIC-KITCHENS Dataset , 2018, ArXiv.
[9] Timo Sztyler,et al. Vision and Acceleration Modalities: Partners for Recognizing Complex Activities , 2019, 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops).
[10] M. Lawton,et al. Assessment of older people: self-maintaining and instrumental activities of daily living. , 1969, The Gerontologist.
[11] Marco Gamba,et al. BORIS: a free, versatile open‐source event‐logging software for video/audio coding and live observations , 2016 .
[12] David Howard,et al. A Comparison of Feature Extraction Methods for the Classification of Dynamic Activities From Accelerometer Data , 2009, IEEE Transactions on Biomedical Engineering.
[13] Timo Sztyler,et al. Hips Do Lie! A Position-Aware Mobile Fall Detection System , 2018, 2018 IEEE International Conference on Pervasive Computing and Communications (PerCom).
[14] Andrea Cavallaro,et al. Hierarchical modeling for first-person vision activity recognition , 2017, Neurocomputing.
[15] Jessica K. Hodgins,et al. Guide to the Carnegie Mellon University Multimodal Activity (CMU-MMAC) Database , 2008 .
[16] Gary M. Weiss,et al. Smartwatch-based activity recognition: A machine learning approach , 2016, 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI).
[17] Deva Ramanan,et al. Detecting activities of daily living in first-person camera views , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[18] Deva Ramanan,et al. Efficiently Scaling up Crowdsourced Video Annotation , 2012, International Journal of Computer Vision.
[19] Christopher G. Atkeson,et al. Assistive intelligent environments for automatic health monitoring , 2005 .
[20] Daqing Zhang,et al. RT-Fall: A Real-Time and Contactless Fall Detection System with Commodity WiFi Devices , 2017, IEEE Transactions on Mobile Computing.
[21] Fernando Fernández Martínez,et al. Feature extraction from smartphone inertial signals for human activity segmentation , 2016, Signal Process..
[22] Takuya Maekawa,et al. Object-Based Activity Recognition with Heterogeneous Sensors on Wrist , 2010, Pervasive.
[23] Daniel Roggen,et al. Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition , 2016, Sensors.
[24] Timo Sztyler,et al. Exploring Semi-Supervised Methods for Labeling Support in Multimodal Datasets , 2018, Sensors.
[25] James M. Rehg,et al. A Scalable Approach to Activity Recognition based on Object Use , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[26] Matthias Rauterberg,et al. The Evolution of First Person Vision Methods: A Survey , 2014, IEEE Transactions on Circuits and Systems for Video Technology.
[27] Timo Sztyler,et al. On-body localization of wearable devices: An investigation of position-aware activity recognition , 2016, 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom).
[28] Gary M. Weiss,et al. Activity recognition using cell phone accelerometers , 2011, SKDD.
[29] Kaiming He,et al. Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[30] Jie Lin,et al. Egocentric activity recognition with multimodal fisher vector , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[31] Seungmin Rho,et al. Physical activity recognition using multiple sensors embedded in a wearable device , 2013, TECS.
[32] Martial Hebert,et al. Temporal segmentation and activity classification from first-person sensing , 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.
[33] Joo-Hwee Lim,et al. Multimodal Multi-Stream Deep Learning for Egocentric Activity Recognition , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[34] Miguel A. Labrador,et al. Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors , 2014, Sensors.
[35] Timo Sztyler,et al. Recognizing Grabbing Actions from Inertial and Video Sensor Data in a Warehouse Scenario , 2017, FNC/MobiSPC.
[36] Markus Vincze,et al. Multi-rate fusion with vision and inertial sensors , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.
[37] Amit Kumar,et al. Combining off-the-shelf Image Classifiers with Transfer Learning for Activity Recognition , 2018, iWOAR.
[38] Susumu Tachi,et al. Pervasive Sensor System for Evidence-based Nursing Care Support , 2006, ICRA.
[39] Jean-Christophe Nebel,et al. Recognition of Activities of Daily Living with Egocentric Vision: A Review , 2016, Sensors.
[40] Gaurav S. Sukhatme,et al. Visual-Inertial Sensor Fusion: Localization, Mapping and Sensor-to-Sensor Self-calibration , 2011, Int. J. Robotics Res..
[41] Sergio Guadarrama,et al. Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[42] Nasser Kehtarnavaz,et al. UTD-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor , 2015, 2015 IEEE International Conference on Image Processing (ICIP).
[43] Timo Sztyler,et al. Unsupervised recognition of interleaved activities of daily living through ontological and probabilistic reasoning , 2016, UbiComp.