Smart surveillance uses artificial intelligence and image processing techniques to automatically detect crimes and security threats from video and audio data collected by surveillance cameras. The system can monitor this data in real-time, detect events like objects entering restricted areas or being left unattended, and notify officials. It employs various technologies like object detection, tracking, classification and database indexing to analyze video and flag relevant events for authorities. While enhancing security, such sophisticated surveillance also raises privacy implications.

2. What does SMART SURVEILLANCE means?????
The proposal is to use the concepts of artificial intelligence
and digital image processing in surveillance systems to
detect crime or crime related events, threats and notify
officials accordingly. The system could monitor the visual
and audio data obtained from the security cameras in-built
with microphones and process them to detect crime or
crime related events and notify the officials accordingly.

3. “Technologies Used in SMART SURVEILLACE”
Plug-and-play analytics frameworks
Object detection and tracking
Alert definition and detection
Object and colour classification
Database event indexing
Search and retrieval

4. Plug-and-play analytics frameworks
Video cameras capture a wide range of
information about people, vehicles, and events.
The type of information captured is dependent
on a number of parameters like camera type,
angle, field of view, and resolution. Automatically
detecting each
type of information requires specialized sets of
algorithms.

5. Object detection and tracking
One of the core capabilities of smart surveillance
systems is the ability to detect and track moving
objects. Object detection algorithms are typically
statistical learning algorithms that dynamically
learn the scene background model and use the
reference model to determine which parts of the
scene correspond to moving objects.

6. Alert definition and detection
Typical smart surveillance systems support a variety of
user-defined behaviour detection capabilities such as
detecting motion within a defined zone, detecting
objects that cross a user defined virtual boundary, and
detecting objects that are abandoned. Graphical user
interface (GUI) tools are used to define zones of
interest, object sizes, and other parameters needed to
define the behaviour.

7. Object and colour classification
Object classification algorithms classify objects
into different classes, for example, People,
Vehicles, Animals, and use training data and
calibration schemes.
Colour classification classifies the dominant colour
of the object into one of the standard colours
(red, green, blue, yellow, black, and white).

8. Database event indexing
The events detected by the video analysis algorithms are
indexed by content and stored in a database. This allows
events to be cross-referenced across multiple spatially
distributed cameras and creates a historical archive of events.
The event index information typically includes time of
occurrence, camera identifier, event type, object type, object
appearance attributes, and an index into the video repository
which allows the user to “play back the relevant video at the
touch of a button.”

9. Search and retrieval
Users can use a variety of GUI tools to define
complex search criteria to retrieve specific
events. Search criteria include, object size,
colour, location in the scene, velocity, time of
occurrence, and several other parameters.
The results of a search can also be rendered in a
variety of summary views.

10. Implications of Smart Surveillance
 Smart surveillance is a technology that has many different
applications and potentially has significant implications to
each of these. We look at implications primarily in the
surveillance application, namely, security and privacy.
 Security Implications: Clearly, the ability to provide real
time alerts, capture high value video and provide
sophisticated forensic video retrieval has the potential to
enhance security in various public and private facilities.

11. IBM Smart Surveillance System

12. References
 An article on Artificial Intelligence
http://www.formal.stanford.edu/jmc/whatisai/whatisai.html
 An article about research involving use of IT for visual
processing in surveillance systems
http://www.phys.org/news/-10-surveillance-tech-carnegie-
mellon.html
 A Oltramari, “Using Ontologies in a Cognitive-Grounded
System”, 2012 STIDS
 An article on use of acoustics in forensics
http://www.acousticassociates.com/ForensicAcousticsSummar
y.aspx
 Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A
Modern Approach (2nd ed.), Upper Saddle River, New Jersey