1. SUREILLANCE IN THE DEPARTMENT THROUGH IMAGE PROCESSING F.Y.P. PRESENTATION BY AHMAD IJAZ & UFUK INCE SUPERVISOR: ASSOC. PROF. ERHAN INCE

2. AIM OF THE PROJECT TECHNIQUES INVOLVED RECORDED AND DETECTED VIDEOS CODES AIM OF THE PROJECT TECHNIQUES INVOLVED *MEDIAN FILTERING *BACKGROUND SUBTRACTION *DILATION *CONNECTED COMPONENT ANALYSIS RECORDED AND DETECTED VIDEOS CODES OUTLINE

3. AIM In this project we will be processing images taken by a wireless camera. Video acquired from the camera will be transferred to the security workstation where the frames extracted from the video sequence will be processed through ‘MALTLAB’. The main purposes are to process the video and perform background subtraction on every frame and do connected component analysis for foreground extraction.

4. WIRELESS CAMERA USED IN OUR FYP *High powered (1500 ft. range) *2.4Ghz wireless weatherproof video/audio security IR installed allows 60 ft. viewing in total darkness. *4-channel receiver and included software allows for direct USB connection to computer. *36 high power IR illuminators and Sony 1/3” CCD, 430 LOR *Features auto scan mode for sequencing up to 4 cameras.

5. Median filtering according to is a nonlinear operation in image processing to reduce "salt and pepper" noise. Each output pixel contains the median value in the m-by-n neighborhood around the corresponding pixel in the input image. medfilt2 pads the image with 0's on the edges, so the median values for the points within [m n]/2 of the edges might appear distorted. MEDIAN FILTERING

6. EFFECT OF MEDIAN FILTERING

7. BACKGROUND SUBTRACTION One of the most important concepts in our project is background subtraction. Background subtraction, takes each frame in the video and subtracts it from a static background that is known prior to the extraction process. Z = imsubtract (X,Y) subtracts each element in array Y from the corresponding element in array X and returns the difference in the corresponding element of the output array Z. X and Y are real.

8. BACKGROUND We have taken first 5 frames without any person and added all those 5 frames and found out the average by dividing it by 5. Now this resultant frame will be considered our background frame from which we will subtract all other coming frames to find out the foreground objects

9. BACKGROUND SUBTRACTION

10. BINARY IMAGE ( THRESHOLD GRAY IMAGE ) RGB IMAGE GRAYSCALE IMAGE AFTER BACKGROUND SUBTRACTION

11. Apply a structuring element to an input image, creating an output image of the same size. CONCEPTS INVOLVED IN DILATION *Structuring Element *Comparing Pixels with its neighborhood in input image *Center Pixel - Origin *Size and Shape of the Neighborhood (1’s & 0’s) of Structuring Element *Padding Behavior DILATION

12. MORPHOLOGICAL DILATION OF BINARY IMAGE Rule in Dilation: Value of the output pixel is the maximum value of all the pixels in the input pixel's neighborhood

13. MORPHOLOGICAL DILATION OF GRAYSCALE IMAGE

14. EFFECT OF DILATION (1)

15. EFFECT OF DILATION (2)

16. CONNECTED COMPONENT ANALYSIS Connected components labeling scans a BINARY image and groups its pixels into components based on pixel connectivity

17. PIXEL CONNECTIVITY

19. TO VISUALIZE LABELING PROCESS

20. RETRIEVE THE COORDINATES (ROWS,COLUMN) EXTRACT (MINR, MINC, MAXR, MAXC) (HEIGHT, WIDTH )

21. BOX THE PERSON AS LONG AS THE PERSON IS STANDING OR MOVING INSIDE OUR REGION

22. MISSION ACCOMPLISHED !! PERSON HAS BEEN

30. END OF PRESENTATION !!

31. THANKING ALL OUR HONORABLE INSTRUCTORS SPECIALLY OUR SUPERVISOR: ASSOC. PROF. DR. ERHAN INCE THE CHAIR: ASSOC. PROF. DR. AYKUT HOCANIN

32. LET’S SEE THE RESULTS !!