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Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
Synesis Embedded Video Analytics
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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 …

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
  • Transcript

    • 1. http://synesis.ru/
      Embedded Video Analytics
      DSP Algorithms forDetection, Tracking and Recognition
    • 2. HD Intelligent Network Video
      Media and Internet
      Face detection and recognition servers
      Intelligent Video Surveillance
      Intelligent cameras, encoders and DVRs
      Digital TV
      DVB receivers,STBs, PVRs,media centres
    • 3. What is the efficiency ofvideo surveillance?
      Quality ofevent recognition
      correct classification
      response time
      documentation
      multiple locations
      Operator comfort
      Cost of ownership
    • 4. Video analytics and video analysis
      ?
    • 5. Functions of video analytics
      Anti-tampering and operability monitoring
      Operational alerts
      Automatic priorities
      Automatic PTZ-camera targeting
      Event recording for instant forensic analysis
      Optimal usage ofnetwork bandwidth and storage memory
    • 6. Solution: embedded video analytics
      Edge device transmits video andmetadata (object and its behaviour description)
      Zone 5intrusiondetected
      VIDEO
      EVENTDATABASE
      EVENT RULES
      METADATA
    • 7. Upon a suspicious event…
      PTZ-targeting
      System notificationover IP network to VMS
      Sound and visual alarms, SMS etc
      ‘Dry contact’ signal
      High quality recording to local or remote storage (NAS)
      Analogue output to legacy systems (matrix or DVR)
    • 8. Embedded vs server analytics
      BOTTLENECK
      camera orencoder
      video management system or DVR
      compressedvideo & audio
      Embedded(edge)analytics
      codecs
      video-analytics
      video management system or DVR
      камера или энкодер
      Server(back-end)analytics
      metadata
      videoanalytics
      video and audio
      codecs
    • 9. Video signal sources
      Network cameraAxis 211A
      Analoguestandard definition cameras(PAL/NTSC)
      Network cameras(standard and highdefinition)
      Thermal cameras
      Thermal cameraTitan-14
    • 10. Wide angle perimeter surveillance(multiple tripwire alert levels)
    • 11. Fence crossing detector
    • 12. Apartment housing event recording
    • 13. Directional detector
    • 14. Running behaviour recognition
    • 15. Time-based loitering behaviour recognition
    • 16. Split target /abandon luggage detection
    • 17. Group people tracking
    • 18. Tampering and malfunction detectors
      Loss of signal
      Obstruction
      Out of focus and lens dusting
      Blackout and overexposure
      AE failure
      Lightingfailure
    • 19. Digital image stabiliser (antishaker)
      Eliminates video shakingcaused by wind and industrial vibrations
      Essential for analytics performance
      Differentiates the camera movementsfrom scene background/foreground movements
    • 20. Video analytics components
    • 21. Object tracker complexity
      complexity
    • 22. Dynamic texture of the real world
    • 23. Dynamic texture modelling
      OBJECT
      HAAR FEATURES
      BACKGROUND
      4D-pyramid
      Featureprobability cloud
      α-channel (mask) for each object
    • 24. People group tracking (Q4 2010)
      Feature cloud enablesobject tracking under partial visibility
      Z-buffer to identify object occlusions
    • 25. Long range intrusion detectionusing directional tripwire
      Unlimited numberof tripwires
      Metadata includetripwire number
      Detection ofunidirectional or bidirectionalcrossing
    • 26. Rule based behaviour recognitionEach zone is configured independently
      Zone entrance
      Zone exist
      Zone loitering:Staying overpredefined period of time
      Zone running:
      Exceeding a predefined speed
      Directional move within zone
    • 27. Metadata sent over IP network / ONVIF
      Event type, data and time
      Zone or tripwire number
      2D object feature:
      Position, size, area, speed
      Real 3D features
      Estimated from 2D featuresusing calibration data
      JPEGframe image withobject trajectory annotation
    • 28. Videoanalytics calibration
      Two human figures define scale & angle
      Drag’n’drop calibration
      Tracking region
      2D to 3D coordinate transform
    • 29. Video analytics parameters
      Service detectors
      Antishaker
      Object tracker
      Contrast sensitivity
      Special sensitivity
      Min. stabilisation time
      Object filters
      Maximum object speed
      Min and max areas
      1
      2
      3
      4
    • 30. Video analytics evaluation
      Methods and results
    • 31. Video analytics public tests
    • 32. Sterile Zone Performance
      38 hours, PAL (720 x 576 x 25 fps), M-JPEG, 40 Mbps
      Number of true positive alarms: a = 432
      False positivesalarms (typeI error): b =2
      False negativesalarms (typeII error): с= 0
    • 33. Object detection range
    • 34. Range doubled with HD analytics
      15-25 m
      20-30 m
      25-45 m
    • 35. Maximum response time
      People walking and running
      2 seconds
      People moving slowly(e.g. crawling)
      10 seconds
    • 36. Causes of false negatives(simple motion detectors)
      Unstable background decreasessensitivity of an adaptive detector
      DYNAMIC TEXTURE MODELING ALGORITHMSENABLE ROBUST OBJECT DETECTION IN A CHALENGING ENVIROMENT
    • 37. Causes of false positives(basic motion detectors)
      Variable lighting
      Shadows from moving clouds and sun
      Moving trees, bushes and water
      Camera shaking
      Animals, birds and insects
      Object trajectory split and double detection
      Snow, rain, fog
    • 38. Examples of false positives(simple motion detectors)
      BIRD
      RABBIT
      INSECT
      CAMERA SHAKING
      VIDEO ANALYTICS PREVENTS FALSE ALARMS CAUSED BY THESE FACTORS
    • 39. Object trackingwhilst tree shadows moving
    • 40. Performance estimation by3D security modeling
      3D modeling
      building infrastructure
      control zones of camerasand third-party detectors
      treats (in space-time)
      Estimation of detection probabilities under variable external conditions
      day/night, fog, snow
      Video presentation
      ORIGINAL BUILDING
      3D MODEL OF BUILDNG
    • 41. Hardware reference designs
      Multifunctional video services and HD cameraswith embedded analytics
    • 42. System-on-chip video analytics
      Videofilters
      Linux
      Video
      analytics
      HD H.264 codec
      1080p
    • 43. Dual channel video analytics encoder
      3/17/2010
      43
      ANALOG + IPHYBRID TECHNOLOGY
      Two analogue inputs (BNC)
      Two managed outputs (BNC)and digital video over IP
      H.264 &MJPEG encoding
      Embedded video & audio analytics
      POE+and backup power
      ONVIF 1.01 support
      - 40⁰...+50⁰С
      Lightning guard
    • 44. HD video analytics camera
    • 45. MJPEG vsH.264 compression
      DATAFLOW, MBPS
      RESOLUTION
    • 46. Unique selling position
      Fully embedded (DSP) implementation
      Real-time processing of uncompressed video
      HD/Megapixel resolution
      Highly scalable
      Unmatched performance in sever environment
      dynamic texture engine
      End-user hardware i-LIDS certification
      on schedule April 2010
      Wide interoperability
      ONVIFcompliance
    • 47. Future of video surveillance
      Multiple camera tracking using 3D model
    • 48. Segmentation problemand object occlusions
      ‘Single camera’video analytics
      ‘Multiple camera’video analytics
      A
      B
      C
      A
    • 49. i-LIDS multiple camera tracking scenario
      2
      3
      4
    • 50. 17/03/2010
      www.synesis.ru
      50
      Video analytics + 3D modeling
      3D model of a buildingand camera controlzones
      1
      2
      Камера 2
      Камера 1
    • 51. OBJECT UNIQUE ID PRESERVED WHEN TRACKING FROM CAMERA TO CAMERA
      17/03/2010
      51
    • 52. 3D trajectory reconstructed frommultiple video sources

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