Video Analytics

- TECHLEAD SOFTWARE
ENGINEERING PVT. LTD.
Why Video Analytics?
 The increasing rate of crime calls for effective security

measures.
 Security Personnel, IP Cameras, CCTV are usually employed

for these reasons.
 But Human vigilance is required in each case which is bound to

induce errors.
Why Video Analytics?
 Manually monitoring CCTV cameras is tedious and monotonous

which effectively reduces productivity.
 Automated surveillance and analytics avoid these errors caused

due to boredom and limited concentration span of humans.
 Video Surveillance and Analytics has gained popularity as

automated solutions are efficient.
Solution by Techlead
• Face

Detection

• License
• Color

Plate Detection

Based Object Tracking

• Restricted
• Object

Zone Intrusion

Recognition
Solution by Techlead

• Configurable Alert System

SMS

Email

Hooter
Feature Applications
 Face Detection
o
o
o

People Counting
Crowd Management
People Loitering

 Object Recognition
o
o
o
o

Abandoned Object Detection
Missing Object Detection
Color Based Object Detection
Directional Movement
Features Application
 Restricted Zone
o
o
o
o

o
o

Intrusion Detection
Geo-Fence Control
Parking and Traffic Management
Traffic violation and Tracking
Stop Light Violation
One way traffic control

 License Plate Detection
 Camera Tampering / Blinding
Features Application
 Customizable user-friendly interface. User can set object

parameters like
o
o
o
o
o

Minimum and maximum size
Color of Object
Location of Object
Template Image
Area of Interest (Restricted Zone)

 Increased Efficiency



Operator
Entire system
How it Works?
Play Video on Click

Frame 1

Frame 2

Frame 3
How it Works?

Frame 1

Frame 2

Frame 3

SMS

Video Analytics
Software

Alerts Engine

Email

Hooter
Alerts Notification
Face Detection
 Detects only Human Faces and Filters out all the other

information content from the Image
 For Intrusion detection- Instead of watching hours of long videos,

searchers can just scan through a few frames (where faces are
detected). This will save lot of time.
 Face is detected through extraction of biological features
 Results – as follows
Face Detection

Faces detected are marked with blue rectangles
License Plate Detection
 This feature of video analytics extracts the License plate region

from any image.
 Applications
 Toll Plaza
 City Surveillance Cameras
 Almost all Security applications

Results - The following slides show license plates detected marked
with a red rectangle.
License Plate Detection
Object Recognition and Tracking
Applications

 Object Tracking in video surveillance:
 The intelligence in video surveillance can be achieved by automating the tasks
of humans. By making the software keep a track on particular specified object
(Car, human etc) this can be achieved.
 Detecting Missing Object in an video frame:
 Same way as above, here the aim is to identify a situation where an object which
was present at a given location is suddenly missing from its position.
 Pattern Recognition :
 Object of an particular shape or size or of a example template is matched with
the live video or captured frames form any camera. E.g. detection of a company
lable in a video / images of variety of products.
Color Based Object Tracking
 This tool can identify an object of a specified set of parameters

from the video.
 User has to select the object of interest from a single reference

frame of the video.

Play Video on Click

Reference frame selected for
parameter settings
Color based Object Tracking Settings
Minimum Size
of Object

Maximum Size
of Object

Reference Image

Color of Object to be
Located:
• R, G, B
• H, S, I
• Pick color from Image
• Pick color from color
palette
Color Based Object Tracking
 All the selected objects are identified in each frame.
 Each object is matched with the specifications of the object of

interest.
 All objects matching those specifications are shortlisted and

tagged for further perusal of the user.
Color Based Object Tracking

Objects identified are marked in Red
Missing Object
 This is an application to avoid theft of stationary objects like a

bag placed somewhere and marked to be guarded or some other
object like public telephone.
 The Object of Interest can be marked by the authorities.
 A video continuously monitoring this object of interest is

processed and analyzed for thefts.
Missing Object Settings

Reference Image

Object of Interest
Missing Object

Object Of Interest
Marked by User

Object Missing
(identified after a few
frames in the video)
Restricted Zone
 This allows user to specify a Restricted Zone from a reference

image.
 Any object trespassing this area will generate an alarm and will

alert the authorities.

Play Video on Click

Reference image of the video
Restricted Zone Settings
(We should use some logical image here)

Reference Image

Restricted Zone
marked in White
Restricted Zone
 Invasion of Privacy and violation of No-Parking Zones etc. can

be detected.
 All Objects in the frame are identified.
 Any object seen in the restricted zone triggers an alarm.
 The authorities are notified of the details of the object in any

mode convenient to them (SMS, Email or Alarm).
Restricted Zone

Fig. a. Original Image

Fig. b. White Area indicates Restricted
Zone

Fig. c. Objects appearing in Restricted
Zone are Marked

Fig. d. Objects appearing in Restricted
Zone are Marked
Object Isolation

Base Image

Image with Objects

Object Isolation
(with shadows)
Smart Feature Extraction

Intelligent Feature extracted (Prominent
colour is extracted while the shadow like
low information regions are removed)
Feature Based Object Recognition
 Main AIM – Detect a given feature (image template) in the

given input image/ video.

 Detection should be independent of size of the feature in

template image and input image/video

 Detection should be independent of the illumination

conditions in the two images

 Object Matching is done on the basis of intelligent

extraction of features.
 (Results ….)
Template
Image

Match This
template image in
the actual Image
i.e. locate the
Fanta Logo in the
entire input Image.

Entire Fanta
Bottle as
Input Image

Matching
The colored lines
drawn across the
images show the
Corresponding feature
Points
The right side image shows the
position where the left side
template image matches it
marked with a blue rectangle
E.g. 2

The colored lines
drawn across the
images join the
feature points
which match
The right side image shows the position where the left
side template image matches it marked with a blue
rectangle
Rotated Bottle

Detection

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Techlead Video Analytics (Image Processing)

  • 1. Video Analytics - TECHLEAD SOFTWARE ENGINEERING PVT. LTD.
  • 2. Why Video Analytics?  The increasing rate of crime calls for effective security measures.  Security Personnel, IP Cameras, CCTV are usually employed for these reasons.  But Human vigilance is required in each case which is bound to induce errors.
  • 3. Why Video Analytics?  Manually monitoring CCTV cameras is tedious and monotonous which effectively reduces productivity.  Automated surveillance and analytics avoid these errors caused due to boredom and limited concentration span of humans.  Video Surveillance and Analytics has gained popularity as automated solutions are efficient.
  • 4. Solution by Techlead • Face Detection • License • Color Plate Detection Based Object Tracking • Restricted • Object Zone Intrusion Recognition
  • 5. Solution by Techlead • Configurable Alert System SMS Email Hooter
  • 6. Feature Applications  Face Detection o o o People Counting Crowd Management People Loitering  Object Recognition o o o o Abandoned Object Detection Missing Object Detection Color Based Object Detection Directional Movement
  • 7. Features Application  Restricted Zone o o o o o o Intrusion Detection Geo-Fence Control Parking and Traffic Management Traffic violation and Tracking Stop Light Violation One way traffic control  License Plate Detection  Camera Tampering / Blinding
  • 8. Features Application  Customizable user-friendly interface. User can set object parameters like o o o o o Minimum and maximum size Color of Object Location of Object Template Image Area of Interest (Restricted Zone)  Increased Efficiency   Operator Entire system
  • 9. How it Works? Play Video on Click Frame 1 Frame 2 Frame 3
  • 10. How it Works? Frame 1 Frame 2 Frame 3 SMS Video Analytics Software Alerts Engine Email Hooter Alerts Notification
  • 11. Face Detection  Detects only Human Faces and Filters out all the other information content from the Image  For Intrusion detection- Instead of watching hours of long videos, searchers can just scan through a few frames (where faces are detected). This will save lot of time.  Face is detected through extraction of biological features  Results – as follows
  • 12. Face Detection Faces detected are marked with blue rectangles
  • 13. License Plate Detection  This feature of video analytics extracts the License plate region from any image.  Applications  Toll Plaza  City Surveillance Cameras  Almost all Security applications Results - The following slides show license plates detected marked with a red rectangle.
  • 15. Object Recognition and Tracking Applications  Object Tracking in video surveillance:  The intelligence in video surveillance can be achieved by automating the tasks of humans. By making the software keep a track on particular specified object (Car, human etc) this can be achieved.  Detecting Missing Object in an video frame:  Same way as above, here the aim is to identify a situation where an object which was present at a given location is suddenly missing from its position.  Pattern Recognition :  Object of an particular shape or size or of a example template is matched with the live video or captured frames form any camera. E.g. detection of a company lable in a video / images of variety of products.
  • 16. Color Based Object Tracking  This tool can identify an object of a specified set of parameters from the video.  User has to select the object of interest from a single reference frame of the video. Play Video on Click Reference frame selected for parameter settings
  • 17. Color based Object Tracking Settings Minimum Size of Object Maximum Size of Object Reference Image Color of Object to be Located: • R, G, B • H, S, I • Pick color from Image • Pick color from color palette
  • 18. Color Based Object Tracking  All the selected objects are identified in each frame.  Each object is matched with the specifications of the object of interest.  All objects matching those specifications are shortlisted and tagged for further perusal of the user.
  • 19. Color Based Object Tracking Objects identified are marked in Red
  • 20. Missing Object  This is an application to avoid theft of stationary objects like a bag placed somewhere and marked to be guarded or some other object like public telephone.  The Object of Interest can be marked by the authorities.  A video continuously monitoring this object of interest is processed and analyzed for thefts.
  • 21. Missing Object Settings Reference Image Object of Interest
  • 22. Missing Object Object Of Interest Marked by User Object Missing (identified after a few frames in the video)
  • 23. Restricted Zone  This allows user to specify a Restricted Zone from a reference image.  Any object trespassing this area will generate an alarm and will alert the authorities. Play Video on Click Reference image of the video
  • 24. Restricted Zone Settings (We should use some logical image here) Reference Image Restricted Zone marked in White
  • 25. Restricted Zone  Invasion of Privacy and violation of No-Parking Zones etc. can be detected.  All Objects in the frame are identified.  Any object seen in the restricted zone triggers an alarm.  The authorities are notified of the details of the object in any mode convenient to them (SMS, Email or Alarm).
  • 26. Restricted Zone Fig. a. Original Image Fig. b. White Area indicates Restricted Zone Fig. c. Objects appearing in Restricted Zone are Marked Fig. d. Objects appearing in Restricted Zone are Marked
  • 27. Object Isolation Base Image Image with Objects Object Isolation (with shadows)
  • 28. Smart Feature Extraction Intelligent Feature extracted (Prominent colour is extracted while the shadow like low information regions are removed)
  • 29. Feature Based Object Recognition  Main AIM – Detect a given feature (image template) in the given input image/ video.  Detection should be independent of size of the feature in template image and input image/video  Detection should be independent of the illumination conditions in the two images  Object Matching is done on the basis of intelligent extraction of features.  (Results ….)
  • 30. Template Image Match This template image in the actual Image i.e. locate the Fanta Logo in the entire input Image. Entire Fanta Bottle as Input Image Matching The colored lines drawn across the images show the Corresponding feature Points
  • 31. The right side image shows the position where the left side template image matches it marked with a blue rectangle
  • 32. E.g. 2 The colored lines drawn across the images join the feature points which match
  • 33. The right side image shows the position where the left side template image matches it marked with a blue rectangle