41,961 research outputs found
Human behavioural analysis with self-organizing map for ambient assisted living
This paper presents a system for automatically classifying the resting location of a moving object in an indoor environment. The system uses an unsupervised neural network (Self Organising Feature Map) fully implemented on a low-cost, low-power automated home-based surveillance system, capable of monitoring activity level of elders living alone independently. The proposed system runs on an embedded platform with a specialised ceiling-mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels and to detect specific events such as potential falls. First order motion information, including first order moving average smoothing, is generated from the 2D image coordinates (trajectories). A novel edge-based object detection algorithm capable of running at a reasonable speed on the embedded platform has been developed. The classification is dynamic and achieved in real-time. The dynamic classifier is achieved using a SOFM and a probabilistic model. Experimental results show less than 20% classification error, showing the robustness of our approach over others in literature with minimal power consumption. The head location of the subject is also estimated by a novel approach capable of running on any resource limited platform with power constraints
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
leave a trace - A People Tracking System Meets Anomaly Detection
Video surveillance always had a negative connotation, among others because of
the loss of privacy and because it may not automatically increase public
safety. If it was able to detect atypical (i.e. dangerous) situations in real
time, autonomously and anonymously, this could change. A prerequisite for this
is a reliable automatic detection of possibly dangerous situations from video
data. This is done classically by object extraction and tracking. From the
derived trajectories, we then want to determine dangerous situations by
detecting atypical trajectories. However, due to ethical considerations it is
better to develop such a system on data without people being threatened or even
harmed, plus with having them know that there is such a tracking system
installed. Another important point is that these situations do not occur very
often in real, public CCTV areas and may be captured properly even less. In the
artistic project leave a trace the tracked objects, people in an atrium of a
institutional building, become actor and thus part of the installation.
Visualisation in real-time allows interaction by these actors, which in turn
creates many atypical interaction situations on which we can develop our
situation detection. The data set has evolved over three years and hence, is
huge. In this article we describe the tracking system and several approaches
for the detection of atypical trajectories
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