3,798 research outputs found
CLAD: A Complex and Long Activities Dataset with Rich Crowdsourced Annotations
This paper introduces a novel activity dataset which exhibits real-life and
diverse scenarios of complex, temporally-extended human activities and actions.
The dataset presents a set of videos of actors performing everyday activities
in a natural and unscripted manner. The dataset was recorded using a static
Kinect 2 sensor which is commonly used on many robotic platforms. The dataset
comprises of RGB-D images, point cloud data, automatically generated skeleton
tracks in addition to crowdsourced annotations. Furthermore, we also describe
the methodology used to acquire annotations through crowdsourcing. Finally some
activity recognition benchmarks are presented using current state-of-the-art
techniques. We believe that this dataset is particularly suitable as a testbed
for activity recognition research but it can also be applicable for other
common tasks in robotics/computer vision research such as object detection and
human skeleton tracking
Open Data, Grey Data, and Stewardship: Universities at the Privacy Frontier
As universities recognize the inherent value in the data they collect and
hold, they encounter unforeseen challenges in stewarding those data in ways
that balance accountability, transparency, and protection of privacy, academic
freedom, and intellectual property. Two parallel developments in academic data
collection are converging: (1) open access requirements, whereby researchers
must provide access to their data as a condition of obtaining grant funding or
publishing results in journals; and (2) the vast accumulation of 'grey data'
about individuals in their daily activities of research, teaching, learning,
services, and administration. The boundaries between research and grey data are
blurring, making it more difficult to assess the risks and responsibilities
associated with any data collection. Many sets of data, both research and grey,
fall outside privacy regulations such as HIPAA, FERPA, and PII. Universities
are exploiting these data for research, learning analytics, faculty evaluation,
strategic decisions, and other sensitive matters. Commercial entities are
besieging universities with requests for access to data or for partnerships to
mine them. The privacy frontier facing research universities spans open access
practices, uses and misuses of data, public records requests, cyber risk, and
curating data for privacy protection. This paper explores the competing values
inherent in data stewardship and makes recommendations for practice, drawing on
the pioneering work of the University of California in privacy and information
security, data governance, and cyber risk.Comment: Final published version, Sept 30, 201
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4th Workshop on human activity sensing corpus and applications: towards open-ended context awareness
Current motion sensors in wearable devices are primarily used for simple orientation and motion sensing. They provide however signals related to more complex and subtle human behaviours which will enable next-generation human-oriented computing in scenarios of high societal value. This requires large scale human activity corpuses and improved methods to recognise activities and their context. This workshop deals with the challenges of designing reproducible experimental setups, running large-scale dataset collection campaigns, designing robust activity and context recognition methods and evaluating systems in the real world. As a special topic, we wish to reflect on the challenges and approaches to recognise activities outside of a pre-defined set to achieve an open-ended activity and context awareness. Following the success of previous years, this workshop is the place to share experiences on human activity corpus and their applications and to discuss the future of activity sensing, in particular towards open-ended contextual intelligence
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