1. L EFT L UGGAGE AND T HEFT -By Mitesh Gupta Shishir Jain

2. M OTIVATION 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.

3. O BJECTIVES : Detection of Left luggage Detection of theft

4. K EYWORDS  Left luggage  Abandonment of luggage by the owner  Attended and unattended luggage  Theft

6. P ROPOSED ALGORITHM :

7. B ACKGROUND SUBTRACTION Background subtraction is done using Gaussian Mixture Model.

8. H EURISTIC 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

9. T RACKING BY MEAN SHIFT ALGORITHM Creates color histogram of a blob. Color histogram is matched in the subsequent images to track the blob.

10. S AMPLE I MAGE AFTER DETECTING LUGGAGE Green Circle = 2m and Red Circle = 3m Person here is in safe radius

11. S AMPLE I MAGE AFTER DETECTING LEFT L UGGAGE Person here is Going into the warning zone an alarm is raised

12. L IMITATION AND P ROBLEMS 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.

13. T HANK YOU