A Comparison of HMMs and Dynamic Bayesian Networks for Recognizing Office Activities Nuria Oliver and Eric Horvitz Adaptive Systems & Interaction, Microsoft Research, Redmond, WA USA {nuria, horvitz}@microsoft.com Abstra

Source URL: www.nuriaoliver.com

• Markov models
• Dynamic Bayesian network
• Hidden Markov model
• Bayesian network
• Activity recognition
• Pattern recognition

Human Activity Recognition using Wearable Devices Sensor Data Zhongyan Wu Shutong Zhang Chenying Zhang Abstract

Source URL: cs229.stanford.edu

Human Activity Recognition using Wearable Devices Sensor Data Shutong Zhang, Jason Wu, Chenying Zhang Problem Formulation Data Preprocessing

Source URL: cs229.stanford.edu

Learning an image-based motion context for multiple people tracking Laura Leal-Taix´e1,2 , Michele Fenzi2 , Alina Kuznetsova2 , Bodo Rosenhahn2 , and Silvio Savarese3 1 2

Source URL: www.tnt.uni-hannover.de

• Artificial intelligence
• Optical flow
• Conference on Computer Vision and Pattern Recognition
• Pedestrian detection
• Activity recognition
• Video tracking

Tool Support for Activity Recognition with Computational Causal Behaviour Models Kristina Yordanova, Frank Kr¨uger, and Thomas Kirste University of Rostock, Institute of Computer Science, MMIS Group, Germany {kristina.y

Source URL: www.dfki.de

Human Activity Recognition: A method combining smartphone and wearable device signals analysis Yinglan Ma, Zhongjie Li, Yunong Jiang DATA

Source URL: cs229.stanford.edu

Action Recognition by Hierarchical Mid-level Action Elements Tian Lan⇤ , Yuke Zhu⇤, Amir Roshan Zamir and Silvio Savarese Stanford University Abstract Realistic videos of human actions exhibit rich spatiotemporal str

Source URL: cvgl.stanford.edu

• IEEE conferences
• Science and technology
• Science in society
• Activity recognition
• Cognition
• Cluster analysis
• Conference on Computer Vision and Pattern Recognition
• Image segmentation
• International Conference on Computer Vision
• Electrical engineering

Activity 17 The Peruvian coin flip—Cryptographic protocols Age group Older elementary and up. Abilities assumed Requires counting, and recognition of odd and even

Source URL: csunplugged.org

Activity 4 Card flip magic—Error detection and correction Age group Middle elementary and up. Abilities assumed Requires counting and recognition of (small) odd and

Source URL: csunplugged.org

12th International Society for Music Information Retrieval Conference (ISMIRTIMBRE AND MELODY FEATURES FOR THE RECOGNITION OF VOCAL ACTIVITY AND INSTRUMENTAL SOLOS IN POLYPHONIC MUSIC Matthias Mauch Hiromasa Fuji

Source URL: ismir2011.ismir.net