https://cs.uwaterloo.ca/~brecht/courses/854-Emerging-2014/readings/iot/iot-survey.pdf

The Internet of Things: A survey

A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

"Gamification" is an informal umbrella term for the use of video game elements in non-gaming systems to improve user experience (UX) and user engagement. The recent introduction of 'gamified' applications to large audiences promises new additions to the existing rich and diverse research on the heuristics, design patterns and dynamics of games and the positive UX they provide. However, what is lacking for a next step forward is the integration of this precise diversity of research endeavors. Therefore, this workshop brings together practitioners and researchers to develop a shared understanding of existing approaches and findings around the gamification of information systems, and identify key synergies, opportunities, and questions for future research.

http://gamification-research.org/wp-content/uploads/2011/04/01-Deterding-Sicart-Nacke-OHara-Dixon.pdf

Gamification. using game-design elements in non-gaming contexts

This highly anticipated new edition provides a comprehensive account of face recognition research and technology, spanning the full range of topics needed for designing operational face recognition systems. After a thorough introductory chapter, each of the following chapters focus on a specific topic, reviewing background information, up-to-date techniques, and recent results, as well as offering challenges and future directions. Features: fully updated, revised and expanded, covering the entire spectrum of concepts, methods, and algorithms for automated face detection and recognition systems; provides comprehensive coverage of face detection, tracking, alignment, feature extraction, and recognition technologies, and issues in evaluation, systems, security, and applications; contains numerous step-by-step algorithms; describes a broad range of applications; presents contributions from an international selection of experts; integrates numerous supporting graphs, tables, charts, and performance data.

/pdf/handbook-of-face-recognition-2d5mzdpu08.pdf

Handbook of Face Recognition

Driving style can characteristically be divided into two categories: “typical” (non-aggressive) and aggressive. Understanding and recognizing driving events that fall into these categories can aid in vehicle safety systems. Potentially-aggressive driving behavior is currently a leading cause of traffic fatalities in the United States. More often than not, drivers are unaware that they commit potentially-aggressive actions daily. To increase awareness and promote driver safety, we are proposing a novel system that uses Dynamic Time Warping (DTW) and smartphone based sensor-fusion (accelerometer, gyroscope, magnetometer, GPS, video) to detect, recognize and record these actions without external processing. Our system differs from past driving pattern recognition research by fusing related inter-axial data from multiple sensors into a single classifier. It also utilizes Euler representation of device attitude (also based on fused data) to aid in classification. All processing is done completely on the smartphone.

Driving style recognition using a smartphone as a sensor platform

The proliferation of accelerometers on consumer electronics has brought an opportunity for interaction based on gestures or physical manipulation of the devices. We present uWave, an efficient recognition algorithm for such interaction using a single three-axis accelerometer. Unlike statistical methods, uWave requires a single training sample for each gesture pattern and allows users to employ personalized gestures and physical manipulations. We evaluate uWave using a large gesture library with over 4000 samples collected from eight users over an elongated period of time for a gesture vocabulary with eight gesture patterns identified by a Nokia research. It shows that uWave achieves 98.6% accuracy, competitive with statistical methods that require significantly more training samples. Our evaluation data set is the largest and most extensive in published studies, to the best of our knowledge. We also present applications of uWave in gesture-based user authentication and interaction with three-dimensional mobile user interfaces using user created gestures.

uWave: Accelerometer-based personalized gesture recognition and its applications

Around the world as both crime and technology become more prevalent, officials find themselves relying more and more on video surveillance as a cure-all in the name of public safety. Used properly, video cameras help expose wrongdoing but typically come at the cost of privacy to those not involved in any maleficent activity. What if we could design intelligent systems that are more selective in what video they capture, and focus on anomalous events while protecting the privacy of authorized personnel? This paper proposes a novel way of combining sensor technology with traditional video surveillance in building a privacy protecting framework that exploits the strengths of these modalities and complements their individual limitations. Our fully functional system utilizes off the shelf sensor hardware (i.e. RFID, motion detection) for localization, and combines this with a XML-based policy framework for access control to determine violations within the space. This information is fused with video surveillance streams in order to make decisions about how to display the individuals being surveilled. To achieve this, we have implemented several video masking techniques that correspond to varying user privacy levels. These results were achievable in real-time at acceptable frame rates, while meeting our requirements for privacy preservation.

/pdf/privacy-protecting-data-collection-in-media-spaces-11q22alilz.pdf

Privacy protecting data collection in media spaces

In this work, we study how Twitter can be used as a sensor to detect frequent and diverse social and physical events in real-time. We devise efficient data collection and event recognition solutions that work despite various limits on free access to Twitter data. We describe a web service implementation of our solution and report our experience with the 2010-2011 US National Football League (NFL) games. The service was able to recognize NFL game events within 40 seconds and with accuracy up to 90%. This capability will be very useful for not only real-time electronic program guide for live broadcast programs but also refined auction of advertisement slots. More importantly, it demonstrates for the first time the feasibility of using Twitter for real-time social and physical event detection for ubiquitous computing.

Human as Real-Time Sensors of Social and Physical Events: A Case Study of Twitter and Sports Games

A security system which is nonintrusive of personal privacy of a person in a space comprises at least a first localization sensor subsystem, if any, in the possession of the person; a video surveillance subsystem arranged and configured to collect visual data related to the person in the space; and a computer subsystem coupled to the localization sensor subsystem and video surveillance subsystem to associate a predetermined privacy level with the localization sensor subsystem, and to provide an access control privilege with the localization sensor subsystem, the computer subsystem determining how to present, store and/or retrieve the visual data while meeting predetermined the privacy level associated with the person.

Apparatus and method for privacy protection of data collection in pervasive environments

A method and apparatus is disclosed whereby the context of user activity can be used to tailor the ambient information system. The method and apparatus use both short-term context such as recent activity and long-term context such as historical patterns to highlight specific content on channels or widgets that are likely to be of most immediate interest to the user. This contextual information provided by the framework can also be used to make intelligent decisions about how to tailor the user experience after a user has interacted with the item in question. Additionally, context information accumulated on one device such as a mobile phone can be broadcasted to other devices to influence the ambient information display application on a second device such as a desktop based on enabling remote access to the local context repository.

Jehan Wickramasuriya

Papers

uWave: Accelerometer-based personalized gesture recognition and its applications

Privacy protecting data collection in media spaces

Human as Real-Time Sensors of Social and Physical Events: A Case Study of Twitter and Sports Games

Apparatus and method for privacy protection of data collection in pervasive environments

Method and apparatus for context-aware delivery of informational content on ambient displays