l eft l uggage and t heft -by mitesh gupta shishir jain
Post on 16-Dec-2015
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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.
KEYWORDS
Left luggageLeft luggage
Abandonment of luggage by the ownerAbandonment of luggage by the owner
Attended and unattended luggageAttended and unattended luggage
Theft Theft
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.
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