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 luggageLeft luggage

Abandonment of luggage by the ownerAbandonment of luggage by the owner

Attended and unattended luggageAttended and unattended luggage

Theft 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