video surveillance using distance maps january 2006 theo schouten harco kuppens egon van den broek
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![Page 1: Video Surveillance using Distance Maps January 2006 Theo Schouten Harco Kuppens Egon van den Broek](https://reader035.vdocuments.mx/reader035/viewer/2022062804/56649d535503460f94a2eee0/html5/thumbnails/1.jpg)
Video Surveillance using Distance Maps
January 2006
Theo SchoutenHarco Kuppens
Egon van den Broek
![Page 2: Video Surveillance using Distance Maps January 2006 Theo Schouten Harco Kuppens Egon van den Broek](https://reader035.vdocuments.mx/reader035/viewer/2022062804/56649d535503460f94a2eee0/html5/thumbnails/2.jpg)
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
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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:
p1% ->background pixel
– random chosenbackground neighbor:p2%->object pixel
– each pixelp3% -> inverse
60 320x240 frames50%-50%-5% noise
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Video Surveillance using Distance Maps
Large sequence
120 640x480 frames once spontaneous movement10%-10%-1% noise of stationary objectchanging number of once a collision moving objects
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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
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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
![Page 7: Video Surveillance using Distance Maps January 2006 Theo Schouten Harco Kuppens Egon van den Broek](https://reader035.vdocuments.mx/reader035/viewer/2022062804/56649d535503460f94a2eee0/html5/thumbnails/7.jpg)
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
![Page 8: Video Surveillance using Distance Maps January 2006 Theo Schouten Harco Kuppens Egon van den Broek](https://reader035.vdocuments.mx/reader035/viewer/2022062804/56649d535503460f94a2eee0/html5/thumbnails/8.jpg)
Video Surveillance using Distance Maps
Output display: objects and distances
60 320x240 frames50%-50%-5% noise
![Page 9: Video Surveillance using Distance Maps January 2006 Theo Schouten Harco Kuppens Egon van den Broek](https://reader035.vdocuments.mx/reader035/viewer/2022062804/56649d535503460f94a2eee0/html5/thumbnails/9.jpg)
Video Surveillance using Distance Maps
Output display: ED map for 1 object
60 320x240 frames50%-50%-5% noise
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Video Surveillance using Distance Maps
Timing
120 640x480 frames
50%-50%-5% noise
AMD
1666 MHz
Intel M725
1600 MHz
Initiali-zation
processing 46.60 ms 27.79 ms
display
generation
10.33 ms 14.54 ms
Per frame
processing 4.94 ms 3.21 ms
display
generation
1.40 ms 1.99 ms
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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
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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.
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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
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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
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Video Surveillance using Distance Maps
The End