The SPHERE Challenge:Activity Recognition with Multimodal Sensor Data

This paper outlines the Sensor Platform for HEalthcare in Residential Environment (SPHERE) project and details the SPHERE challenge that will take place in conjunction with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML-PKDD) between March and July 2016. The SPHERE challenge is an activity recognition competition where predictions are made from video, accelerometer and environmental sensors. Monetary prizes will be awarded to the top three entrants, with Euro 1,000 being awarded to the winner, Euro 600 being awarded to the first runner up, and Euro 400 being awarded to the second runner up.Comment: Paper describing dataset. 11 pages; 4 figure

DS-KCF tracker code (BMVC'15 version)

This repository contains the source code (Matlab version 1.0) of the DS-KCF tracker published in BMVC'15. M. Camplani, S. Hannuna, D. Damen, M. Mirmehdi, A. Paiment, L. Tao, T. Burghard. Robust Real-time RGB-D Tracking with Depth Scaling Kernelised Correlation Filters and Occlusion Handling, BMVC 201

DS-KCF tracker code (BMVC'15 version)

This repository contains the source code (Matlab version 1.0) of the DS-KCF tracker published in BMVC'15. M. Camplani, S. Hannuna, D. Damen, M. Mirmehdi, A. Paiment, L. Tao, T. Burghard. Robust Real-time RGB-D Tracking with Depth Scaling Kernelised Correlation Filters and Occlusion Handling, BMVC 201

SPHERE_H130_dataset

The dataset contains both RGB and depth images, and the data from two accelerometers for activity recognition in home environments. The dataset was presented in: L. Tao, T. Burghardt, S. Hannuna, M. Camplani, A. Paiement, D. Damen, M. Mirmehdi, I. Craddock. A Comparative Home Activity Monitoring Study using Visual and Inertial Sensors, 17th International Conference on E-Health Networking, Application and Services, 635-638, 2015. (The size of the repository is relatively big. We suggest users who do not need whole dataset downloading each folder separately.) Access to this dataset is restricted due to the involvement of identifiable participants

Depth video and skeleton of people walking up stairs

This dataset contains sequences of depth images of people walking up stairs, as well as the associated skeletons obtained from the OpenNI SDK. These data have been acquired in the frame of the SPHERE IRC for the experiments on movement quality assessment in: A. Paiement, L. Tao, M. Camplani, S. Hannuna, D. Damen, M. Mirmehdi, Online quality assessment of human movement from skeleton data, in Proceedings of BMVC 2014