Visual sensor networks are networks of smart camera devices that can process and fuse images from multiple viewpoints. They face challenges due to limited resources but enable applications like surveillance. Visual sensor nodes capture images locally and transmit extracted data rather than raw images. Effective processing algorithms are needed to handle large amounts of visual data within the nodes' constraints.

2. Hello Guys 
 Group Member:
Syed Muhammad Bilal
Syed Umar Farooq

3. Topic
 Adaptive Learning
through Visual Sensor
Networks

4. Introduction
 A visual sensor network is a network of
spatially distributed smart camera devices
capable of processing and fusing images of a
scene from a variety of viewpoints into some
form more useful than the individual images.

5. What is Sensor?
 A device which detects or measures a physical
property and records, indicates, or otherwise
responds to it.

6. Types of Sensor?
 The most frequently used different types of
sensors are classified based on the quantities
such as Electric current or Potential or
Magnetic or Radio sensors, Humidity sensor,
Fluid velocity or Flow sensors, Pressure
sensors, Thermal or Heat or Temperature
sensors, Proximity sensors, Optical
sensors,Position sensors

7. Characteristics of Visual Sensor
Networks
 Data Storage
 Local Processing
 Time Synchronization
 Autonomous Camera
Collaboration

8. Visual Sensor Nodes Architecture
 Visual sensor nodes are defined as small, battery-
operated nodes with image sensor, embedded
processor, and wireless transceiver. This allows them to
perform the following operations:
 capture images or videos from the scene, process them
locally
 to extract relevant information, and transmit this data
rather than raw images to a base station for activity
analysis.
 The sensing module consists of one or more imaging
sensors to capture image and video sequences. Most
embedded platforms have an integrated Complementary

9. Visual Sensor Nodes Architecture
 Metal–Oxide–Semiconductor (CMOS) imaging sensor. Although Charge-
Coupled
 Device (CCD) components were the premier image capture technology, by
2004,
 CMOS sensors officially surpassed them as the overall image capture
technology
 of choice especially for constrained environments This is mainly due to
 the low power consumption of CMOS (one tenth of CCD), low cost, and
easy
 integration of all camera functions on a single chip, significantly reducing
chip
 count and board space [15]. Moreover, CMOS sensors offer the same or
better
 sensitivity compared to CCDs.

10. VSN Challenges
 VSNs are capable of collecting large volumes
of data about monitored scenes
 but are constrained with the available node
resources and network bandwidth.
 Designing and implementing VSNs is thus
faced with several challenges.

11. Visual Data Processing
 Object vision is one of the key features of VSNs marking it from
other sensor-based systems. Visual sensors are capable of
capturing large amounts of images of the monitored scene. Raw
images are processed locally so only partial useful data is sent to
the central station for further analysis or other nodes for
collaborative processing.
 Performing all this on-board is very challenging given the limited
resources of these nodes (power and memory).Conventional
processing algorithms intended for massive computers cannot be
applied here. They must be modified and adapted

12. Research in Visual Sensor
Networks
 Visual sensor networks are based on several
diverse research fields, including image/vision
processing, communication and networking,
and distributed and embedded system
processing. Thus, the design complexity
involves finding the best tradeoff between
performance and different aspects of these
networks

13. Applications
 Surveillance
 Environmental monitoring
 Smart homes
 Smart meeting rooms
 Telepresence system

14. Conclusion
 WVSNs are next generation WSNs.
 It introduce new challenges and research
opportunities.
 It enables more visual data-based applications
such as security surveillance, home care and
environment monitoring.