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# new Left Luggage and Theft

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```									LEFT LUGGAGE AND THEFT

-By
Mitesh Gupta
Shishir Jain
MOTIVATION
   In recent years the demand on video
analysis application such as video
surveillance is growing rapidly. Video
surveillance is commonly used in security
systems, but requires more intelligent and
more robust technical approaches. Such
systems, used in airports, train stations or
other public spaces, can bring security to a
higher level.
OBJECTIVES:

 Detection of Left luggage
 Detection of theft
KEYWORDS

   Left luggage

   Abandonment of luggage by the owner

   Attended and unattended luggage

   Theft
PROPOSED ALGORITHM:
BACKGROUND SUBTRACTION
Background subtraction is done using Gaussian
Mixture Model.
HEURISTIC APPROACH                FOR DETECTION OF
OWNER AND LUGGAGE

   Luggage
   Predefined height to width ratio
   Range of height (55,75)pixels
   Range of width (60,85)pixels
   Owner
   Width to height ratio should lie between 0.3 to 0.8
   Minimum height of 120 pixels
   Minimum width of 40 pixels
TRACKING BY MEAN SHIFT ALGORITHM
 Creates color histogram of a blob.
 Color histogram is matched in the subsequent
images to track the blob.
SAMPLE IMAGE AFTER         DETECTING
LUGGAGE

Green Circle = 2m and Red Circle = 3m
Person here is in safe radius
SAMPLE IMAGE AFTER           DETECTING LEFT
LUGGAGE

Person here is Going into the warning zone an alarm
is raised
LIMITATION AND PROBLEMS
 After BG subtraction the left luggage becomes
BG object and is forgotten by GMM model.
 Tracking part fails in case of occlusion.

 Identification of owner: no appropriate method
found till now.
 Luggage is identified using heuristic approach.

 During theft, if thief is occluded then it is difficult
to analyze the theft.
THANK YOU

```
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