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Surveillance
Hybrid-Resolution Mote for Surveillance
Click here for a description of Stanford MeshEye mote
Face Profile Collection and Tracking
Distributed Intelligent Surveillance
In most today's implementation of surveillance systems, pan-tilt-zoom cameras are distributed across the deployment area and their raw video output is streamed to a surveillance center, in which a panel of monitors displays the video streams. Obviously, this implementation requires sufficient bandwidth for video streaming, has high installation cost, and most of all is hardly scalable. We consider any surveillance solution that performs processing of the video stream right at the camera and hence reduces bandwidth requirements as an intelligent system.
As a first level of intelligence, the camera nodes use a motion detection scheme such that only moving scenes are streamed to the surveillance center. At a second level, the camera nodes could perform object detection and classification such that only moving scenes containing persons or more general objects of interest are forwarded. Going even further, the smart camera nodes could collaborate to identify objects and only transmit their textual description along with a snapshot. Continuing this train of thought of adding intelligence to surveillance, a network of smart cameras could possibly just notify the surveillance center in case of events of interest by providing a hybrid textual/visual or fully textual description of the event. As the level of intelligence increases, bandwidth requirements on the underlying data transmission network decrease accordingly.
Attribute-based Tracking
This project presents an implementation of a color-based multiple agent tracking algorithm targeted for wireless image sensor networks. It uses multiple image sensors to track and graphically display the path of autonomous agents moving across the overlapping fields of view (FOV) of the sensors. A color histogram is constructed for each agent to identify the dominant hue of each agent, and this hue value is used as a means of identification. The algorithm is able to reliably track the agents when collisions occur and to locate the relative position of each agent during collisions. This work also studies the possible use of color histograms in image sensor color balance self-calibration, which would be a possible future extension of the project. This algorithm has low computational requirements and its complexity scales linearly with the size of the network, so it is feasible in low-power, large-scale wireless sensor networks.
Event-driven Routing
Security monitoring systems are deployed specifically to detect
events that occur rarely but require immediate notification.
On the other hand, the design of sensor network-based
systems that comprise energy constrained nodes is typically
dictated by bandwidth efficiency and longevity concerns.
Therefore, the design of such systems must not only strive to
provide packet delivery guarantees over potentially multiple
hops, but also consider dynamically changing parameters at network nodes such as queue sizes.
In security monitoring systems,
the observer may not be interested in all images but only the
images of certain events. Detection of situations that may
need the observer's attention or intervention, monitoring
the rate at which moving objects flow through the observed
environment, or registering the types and quantities
of certain objects or events are among such applications.
These applications may only require occasional transmission
of images to the observer. For example, in an application to
monitor an environment, the nodes of the network periodically
transmit packets to acknowledge that it is alive to base
station. Occasionally, when a node detects a suspicious target,
it may buffer and transmit a few image frames, which
can be used for further analysis.
In this project a distributed routing scheme with adjustable
priority support for event-driven wireless surveillance networks is developed.
The proposed algorithm employs a cost function
based on the network topology, current queue lengths, and
remaining energies at neighboring nodes as a basis for next
hop selection, and can provide improved end-to-end delay
and lifetime performance. The network nodes are assumed
to generate periodic data packets that are reported to the
destination via multihop routing. Nodes may also infrequently
detect an event from which a set of image frames
need to be reported. In order to report the observed events
on time, we assign different priorities to the image packets
based on the importance of the packets such that the
observer can still obtain high quality images when we have
delay constraints in the base station. Simulation results
indicate lower end-to-end average and maximum
delays, significantly reduced buffer size requirements
for the network nodes, and improved network lifetime. In
addition, the proposed algorithm can provide
better quality images in terms of the average peak signal-to-
noise ratio of the transmitted images.

Publications
Application-Oriented Design of Smart Camera Networks
S. Hengstler and H. Aghajan,
1st Int. Conf. on Distributed Smart Cameras (ICDSC), Sept. 2007.
Architecture for Cluster-based Automated Surveillance Network for Detecting and Tracking Multiple Persons
R. Goshorn, J. Goshorn, D. Goshorn, and H. Aghajan,
1st Int. Conf. on Distributed Smart Cameras (ICDSC), Sept. 2007.
A LQR Spatiotemporal Fusion Technique for Face Profile Collection in Smart
Camera Surveillance
C. Chang and H. Aghajan,
Int. Conf. on Advanced Video and Signal based Surveillance (AVSS), Sept. 2007.
Spatiotemporal Fusion Framework for Multi-Camera Face Orientation Analysis
C. Chang and H. Aghajan,
Advanced Concepts for Intelligent Vision Systems (ACIVS), August 2007.
MeshEye: A Hybrid-Resolution Smart Camera Mote for Applications in Distributed Intelligent Surveillance
S. Hengstler, D. Prashanth, S. Fong, and H. Aghajan,
Information Processing in Sensor Networks (IPSN-SPOTS), April 2007.
A Smart Camera Mote Architecture for Distributed Intelligent Surveillance
S. Hengstler and H. Aghajan,
ACM SenSys Workshop on Distributed Smart Cameras (DSC), Oct. 2006.
Color-Based Multiple Agent Tracking for Wireless Image Sensor Networks
E. Oto, F. Lau, and H. Aghajan,
Advanced Concepts for Intelligent Vision Systems (ACIVS), Sept. 2006.
Collaborative Node Localization in Surveillance Networks using Opportunistic Target Observations
H. Lee and H. Aghajan,
ACM Multimedia Workshop On Video Surveillance and Sensor Networks (VSSN), Oct. 2006.
Event-Driven Geographic Routing for Wireless Image Sensor Networks
L. Savidge, H. Lee, H. Aghajan, and A. Goldsmith,
COGnitive systems with Interactive Sensors (COGIS), March 2006.
QoS-Based Geographic Routing for Event-Driven Image Sensor Networks
L. Savidge, H. Lee, H. Aghajan, and A. Goldsmith,
Broadband Advanced Sensor Networks (BaseNets), Oct. 2005.