Synesis Embedded Video Analytics


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A set of new video analytics algorithms is described for automatic object detection and rule-based event recognition. The algorithms utilizes a 4D feature pyramid to model objects and the background in HD. A commercial version based TI's DaVinci DSP is embedded in intelligent IP-cameras and video encoders.

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  • Мониторинг работоспособности и автоматическое обнаружение несанкционированных манипуляций с камерой
  • Synesis Embedded Video Analytics

    1. 1.<br />Embedded Video Analytics<br />DSP Algorithms forDetection, Tracking and Recognition<br />
    2. 2. HD Intelligent Network Video<br />Media and Internet<br />Face detection and recognition servers<br />Intelligent Video Surveillance<br />Intelligent cameras, encoders and DVRs<br />Digital TV<br />DVB receivers,STBs, PVRs,media centres<br />
    3. 3. What is the efficiency ofvideo surveillance?<br />Quality ofevent recognition<br />correct classification<br />response time<br />documentation<br />multiple locations<br />Operator comfort<br />Cost of ownership<br />
    4. 4. Video analytics and video analysis<br />?<br />
    5. 5. Functions of video analytics<br />Anti-tampering and operability monitoring<br />Operational alerts<br />Automatic priorities<br />Automatic PTZ-camera targeting<br />Event recording for instant forensic analysis<br />Optimal usage ofnetwork bandwidth and storage memory <br />
    6. 6. Solution: embedded video analytics<br />Edge device transmits video andmetadata (object and its behaviour description)<br />Zone 5intrusiondetected<br />VIDEO<br />EVENTDATABASE<br />EVENT RULES<br />METADATA<br />
    7. 7. Upon a suspicious event…<br />PTZ-targeting<br />System notificationover IP network to VMS<br />Sound and visual alarms, SMS etc<br />‘Dry contact’ signal<br />High quality recording to local or remote storage (NAS)<br />Analogue output to legacy systems (matrix or DVR)<br />
    8. 8. Embedded vs server analytics<br />BOTTLENECK<br />camera orencoder<br />video management system or DVR<br />compressedvideo & audio<br />Embedded(edge)analytics<br />codecs<br />video-analytics<br />video management system or DVR<br />камера или энкодер<br />Server(back-end)analytics<br />metadata<br />videoanalytics<br />video and audio<br />codecs<br />
    9. 9. Video signal sources<br />Network cameraAxis 211A<br />Analoguestandard definition cameras(PAL/NTSC)<br />Network cameras(standard and highdefinition)<br />Thermal cameras<br />Thermal cameraTitan-14<br />
    10. 10. Wide angle perimeter surveillance(multiple tripwire alert levels)<br />
    11. 11. Fence crossing detector<br />
    12. 12. Apartment housing event recording<br />
    13. 13. Directional detector<br />
    14. 14. Running behaviour recognition<br />
    15. 15. Time-based loitering behaviour recognition<br />
    16. 16. Split target /abandon luggage detection<br />
    17. 17. Group people tracking<br />
    18. 18. Tampering and malfunction detectors<br />Loss of signal<br />Obstruction<br />Out of focus and lens dusting<br />Blackout and overexposure <br />AE failure<br />Lightingfailure<br />
    19. 19. Digital image stabiliser (antishaker)<br />Eliminates video shakingcaused by wind and industrial vibrations <br />Essential for analytics performance<br />Differentiates the camera movementsfrom scene background/foreground movements<br />
    20. 20. Video analytics components<br />
    21. 21. Object tracker complexity<br />complexity<br />
    22. 22. Dynamic texture of the real world<br />
    23. 23. Dynamic texture modelling<br />OBJECT<br />HAAR FEATURES<br />BACKGROUND<br />4D-pyramid<br />Featureprobability cloud<br />α-channel (mask) for each object<br />
    24. 24. People group tracking (Q4 2010)<br />Feature cloud enablesobject tracking under partial visibility<br />Z-buffer to identify object occlusions<br />
    25. 25. Long range intrusion detectionusing directional tripwire<br />Unlimited numberof tripwires<br />Metadata includetripwire number<br />Detection ofunidirectional or bidirectionalcrossing<br />
    26. 26. Rule based behaviour recognitionEach zone is configured independently<br />Zone entrance<br />Zone exist<br />Zone loitering:Staying overpredefined period of time<br />Zone running:<br />Exceeding a predefined speed<br />Directional move within zone<br />
    27. 27. Metadata sent over IP network / ONVIF<br />Event type, data and time<br />Zone or tripwire number<br />2D object feature:<br />Position, size, area, speed<br />Real 3D features<br />Estimated from 2D featuresusing calibration data<br />JPEGframe image withobject trajectory annotation<br />
    28. 28. Videoanalytics calibration<br />Two human figures define scale & angle<br />Drag’n’drop calibration<br />Tracking region<br />2D to 3D coordinate transform<br />
    29. 29. Video analytics parameters<br />Service detectors<br />Antishaker<br />Object tracker<br />Contrast sensitivity<br />Special sensitivity<br />Min. stabilisation time<br />Object filters<br />Maximum object speed<br />Min and max areas<br />1<br />2<br />3<br />4<br />
    30. 30. Video analytics evaluation<br />Methods and results<br />
    31. 31. Video analytics public tests<br />
    32. 32. Sterile Zone Performance<br />38 hours, PAL (720 x 576 x 25 fps), M-JPEG, 40 Mbps<br />Number of true positive alarms: a = 432<br />False positivesalarms (typeI error): b =2<br />False negativesalarms (typeII error): с= 0<br />
    33. 33. Object detection range<br />
    34. 34. Range doubled with HD analytics<br />15-25 m<br />20-30 m<br />25-45 m<br />
    35. 35. Maximum response time<br />People walking and running<br />2 seconds<br />People moving slowly(e.g. crawling)<br />10 seconds<br />
    36. 36. Causes of false negatives(simple motion detectors)<br />Unstable background decreasessensitivity of an adaptive detector<br />DYNAMIC TEXTURE MODELING ALGORITHMSENABLE ROBUST OBJECT DETECTION IN A CHALENGING ENVIROMENT<br />
    37. 37. Causes of false positives(basic motion detectors)<br />Variable lighting<br />Shadows from moving clouds and sun<br />Moving trees, bushes and water<br />Camera shaking<br />Animals, birds and insects<br />Object trajectory split and double detection<br />Snow, rain, fog<br />
    38. 38. Examples of false positives(simple motion detectors)<br />BIRD<br />RABBIT<br />INSECT<br />CAMERA SHAKING<br />VIDEO ANALYTICS PREVENTS FALSE ALARMS CAUSED BY THESE FACTORS<br />
    39. 39. Object trackingwhilst tree shadows moving<br />
    40. 40. Performance estimation by3D security modeling<br />3D modeling<br />building infrastructure<br />control zones of camerasand third-party detectors<br />treats (in space-time)<br />Estimation of detection probabilities under variable external conditions<br />day/night, fog, snow<br />Video presentation<br />ORIGINAL BUILDING<br />3D MODEL OF BUILDNG<br />
    41. 41. Hardware reference designs<br />Multifunctional video services and HD cameraswith embedded analytics<br />
    42. 42. System-on-chip video analytics<br />Videofilters<br />Linux<br />Video<br />analytics<br />HD H.264 codec<br />1080p<br />
    43. 43. Dual channel video analytics encoder<br />3/17/2010<br />43<br />ANALOG + IPHYBRID TECHNOLOGY<br />Two analogue inputs (BNC)<br />Two managed outputs (BNC)and digital video over IP<br />H.264 &MJPEG encoding<br />Embedded video & audio analytics<br />POE+and backup power<br />ONVIF 1.01 support<br />- 40⁰...+50⁰С<br />Lightning guard<br />
    44. 44. HD video analytics camera<br />
    45. 45. MJPEG vsH.264 compression<br />DATAFLOW, MBPS<br />RESOLUTION<br />
    46. 46. Unique selling position<br />Fully embedded (DSP) implementation<br />Real-time processing of uncompressed video<br />HD/Megapixel resolution<br />Highly scalable<br />Unmatched performance in sever environment<br />dynamic texture engine<br />End-user hardware i-LIDS certification<br />on schedule April 2010<br />Wide interoperability<br />ONVIFcompliance<br />
    47. 47. Future of video surveillance<br />Multiple camera tracking using 3D model<br />
    48. 48. Segmentation problemand object occlusions<br />‘Single camera’video analytics<br />‘Multiple camera’video analytics<br />A<br />B<br />C<br />A<br />
    49. 49. i-LIDS multiple camera tracking scenario<br />2<br />3<br />4<br />
    50. 50. 17/03/2010<br /><br />50<br />Video analytics + 3D modeling<br />3D model of a buildingand camera controlzones<br />1<br />2<br />Камера 2<br />Камера 1<br />
    52. 52. 3D trajectory reconstructed frommultiple video sources<br />