1. Surveillance Application Ankit Mathur Mayank Agarwal Subhajit Sanyal Lavanya Sharan Vipul Kansal

2. Introduction Two camera-based surveillance system Has an initial learning phase Can detect and segment out intruders For a single intruder, provides Foot location Height

3. Learning Phase Can be implemented as static/dynamic We have implemented static learning Background is monitored initially For each pixel,  and  may be maintained under Gaussian model We just maintain max. min. and maximum inter-frame difference for RGB separately

4. Background Subtraction Can be based on YCbCr to monitor chromaticity & ignore intensity changes We have implemented RGB monitoring If RGB lie within allowed range for a pixel, it is blackened (background) If not within range, the pixel is in foreground

5. Background subtracted image

6. Shadow removal by stereo Shadow of foreground objects in the background-subtracted images Need to eliminate shadows for finding foot position & other state information Use two cameras Using ground-plane homography, warp and subtract out shadows on the ground

7. Original Image – Warped Image

8. Noise removal Lot of small speckles in the image observed These speckles may disturb the intensity centroid and need to be removed Carry out smoothening by blurring / median filtering We implement median filtering

9. Image with noise

10. Smoothened Image

11. Detecting foreground object Present implementation assumes only one foreground object Carry out a search along the line passing through centroid in the vertical vanishing direction We use a variant of DDA algorithm and find head & foot on the line

12. Compute statistics The camera is pre-calibrated using the two-plane registration method Knowing the head and foot in the image the actual height and foot location can be computed

15. Code organization

16. Learning Grabber Back-subt Grabber Video 1 Video 2 Learning Back-subt Shadow Subtraction Grnd-Plane Homography Height Foot Median Filter

17. Meeting real-time constraints Use MMX ® instructions to speed up code (process 64-bits in a go) After warping stage, image is binary and is stored as 1-bit per pixel For operations like median filtering, 64 pixels processed simultaneously Separate grabbing and processing threads