1. Video Surveillance using Distance Maps January 2006 Theo Schouten Harco Kuppens Egon van den Broek

2. Video Surveillance using Distance Maps Video Surveillance fast growing sector in security market fundamental issues and challenges –interpretation, generality, automation, efficiency, robustness, trade off, performance evaluation, multiple camera and data fusion, feature selection and integration (Amer and Regazzoni) efficiency (real-time) and robustness single camera, top view moving+stationary objects detect objects, measure distances + motion abstract from color to binary conversion –model imperfections: changing illumination, shadows, video noise

3. Video Surveillance using Distance Maps Operational environment virtual, dynamic robot navigation environment –binary frames with moving+stationary objects using Macromedia Flash noise model –border object pixel: p 1 % ->background pixel –random chosen background neighbor: p 2 %->object pixel –each pixel p 3 % -> inverse 60 320x240 frames 50%-50%-5% noise

4. Video Surveillance using Distance Maps Large sequence 120 640x480 framesonce spontaneous movement 10%-10%-1% noiseof stationary object changing number ofonce a collision moving objects

5. Video Surveillance using Distance Maps Fast Exact Euclidean Distance (FEED) Maps D(p) = if (p  O) then 0 else  each q  O feeds its ED to each p: D(p) = min ( D(p), ED(q,p)) (10-20 ms, factor 2 slower than chamfer 3,4) ED map stationary objects only: –loop over border moving object: ED to stationary objects ED stat+moving =min(ED stat,ED moving ) (0.5-1 ms, factor 2 faster than chamfer 3,4) –input to ”robot” objects border pixels bisection lines precalculate ED

6. Video Surveillance using Distance Maps Real-time and exact motion detection initialization: n (5) frames to locate stationary pixels per frame: –determine pixels of stationary and moving objects –check for a movement of stationary objects –locate moving objects –calculate distances –generate output (application dependent) list of tracked (frame-to-frame) objects+distances graphical display of objects+distance for 1 “robot”: ED map of stationary and other moving objects

7. Video Surveillance using Distance Maps Design guidelines for speed pre-calculate –data structures depending only on stationary obj. avoid data movement –keep track of added moving object data –reinitialize only changed parts minimize loops and test –combine logically distinct program parts –split a logical function over program parts use the right level of abstraction –stationary: pixels; moving: objects

8. Video Surveillance using Distance Maps Output display: objects and distances 60 320x240 frames 50%-50%-5% noise

9. Video Surveillance using Distance Maps Output display: ED map for 1 object 60 320x240 frames 50%-50%-5% noise

10. Video Surveillance using Distance Maps Timing 120 640x480 frames 50%-50%-5% noise AMD 1666 MHz Intel M725 1600 MHz Initiali- zation processing46.60 ms27.79 ms display generation 10.33 ms14.54 ms Per frame processing 4.94 ms 3.21 ms display generation 1.40 ms 1.99 ms

11. Video Surveillance using Distance Maps Details: locating stationary object pixels moving objects should move sufficiently fast: –no overlap in at least 2 frames if not: –program keeps running –but too often in initialization phase further strategies: –adapt number of initialization frames –more elaborate statistical processing –towards object detection

12. Video Surveillance using Distance Maps Details: minimum movement stationary objects red: disappeared stationary object pixels 22, 54 and 73 (least noise sequences: 36,99 and 92) maximum red pixels due to noise: 2 (0) able to detect very small movements robustly dependent on “imperfection and noise” model: –not direction dependent, no form change strategies: skip frames, appearing object pixels, etc.

13. Video Surveillance using Distance Maps Details: minimum size moving objects “hole” noise objects: –removed by a simple, fast method –in theory pathological cases where this will fail other noise objects: removed by threshold on size –contour size: noise maximal 9, minimal object: 42 –moving objects can be factor 3 smaller part input frame red= moving color: border moving

14. Video Surveillance using Distance Maps Conclusion real-time, robust object, distance and motion detection –well defined environment, with limitations –using distance maps generated by FEED –providing output for surveillance purposes design guidelines to achieve our results discussed 3 restrictions on content of frames pointers to further research –reduce the restrictions –enlarge variability of environment simulated environment with other “noise” models real video camera input

15. Video Surveillance using Distance Maps The End