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Automated video surveillance: challenges and solutions. ACE Surveillance (Annotated Critical Evidence) case study.

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Automated video surveillance: challenges and solutions. ACE Surveillance (Annotated Critical Evidence) case study. Dmitry Gorodnichy and Tony Mungham – PowerPoint PPT presentation

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Title: Automated video surveillance: challenges and solutions. ACE Surveillance (Annotated Critical Evidence) case study.


1
Automated video surveillance challenges and
solutions. ACE Surveillance (Annotated Critical
Evidence) case study.
  • Dmitry Gorodnichy and Tony Mungham
  • Laboratory Scientific Services
    Directorate Canada Border Services
    Agency www.videorecognition.com/ACE

2
Outline
  • Problems with status-quo Video Surveillance
  • Real-time and archival problems
  • Operational considerations
  • Next generation solution - Video Analytics based
  • Motion detection myth and problem
  • Object detection as example of real
    intelligence
  • ACE Surveillance first fully-functional object-
    detection-based prototype
  • Year long tests with different levels of
    complexity
  • What that means for future of Video Surveillance
  • Conclusions

3
Role of Video Technology (VT)
  • In the context of enhancing security, Video
    Technology (VT) is one of the most demanded
    technologies of the 21st century
  • It is publicly acceptable
  • It provides rich in content data
  • Multi-million funding in Canada and worldwide
  • CBSA Port Runner project invested 10s of Millions
    in CCTV upgrades
  • Transport Canada opens 35M of funding towards
    procurement of CCTV

4
VT at CBSA
  • CBSA is a major user of CCTV systems at POEs
  • Most major CCTV installations start to leverage
    VT
  • Current task to lead applied RD to push VT to
    help CBSA apply ST innovative approaches to
    border management
  • Event detection and notification to provide
    effective response to events
  • Traffic trends analysis to assist with border
    management
  • Video storage management to manage the cost of
    storage and meet obligations under the privacy
    act
  • Data integration/fusion of contextualised video
    information

5
Problem with status quo use of CCTV surveillance
  • Modes of operation
  • Active - personnel watch video at all times
  • Passive - in conjunction with other duties
  • Archival - for post-event analysis
  • Current systems and protocols are not efficient
    in either mode!
  • Problem in real-time modes an event may easily
    pass unnoticed .
  • due to false or simultaneous alarms,
  • lack of time needed to rewind and analyse all
    video streams.

6
Problems in Archival mode
  • Due to temporal nature of data
  • Storage space consumption problem
  • Typical assignment
  • 2-16 cameras, 7 or 30 days of recording, 2-10 Mb
    / min.
  • ?1.5 GB per day per camera / 20 - 700 GB total
    !
  • Data management and retrieval problem
  • London bombing video backtracking experience
  • Manual browsing of millions of hours of
    digitized video from thousands of cameras proved
    impossible within time-sensed period by the
    Scotland Yard trying to back-track the suspects

7
Operational considerations
  • Lots of CCTV infrastructure Many local
    initiatives, not coordinated
  • Most video technology decisions are influenced by
    vendors - short-term solutions
  • Over 30 different video systems within the same
    dept. (at RCMP)
  • A national program with proper benchmark-based
    planning and evaluation of VT is required
  • Leveraging advances recently made in ST
  • Technical standards for capturing /saving video
    data.
  • Policies in when, where and how VT should be used.

8
Video Technology today
Video Analytics (Video Recognition)
21st century
Wireless, Network Connected (IP)
Digital
Analog
First video recording
20th century
9
Next generation Video Technology
  • Is Video Analytics based
  • also identified as
  • Video Recognition,
  • Intelligent Video,
  • Smart Video / Smart Camera
  • Video Analysis Content Extraction
  • Perceptual Vision
  • is not much about capturing better data (better
    lenses, grabbers, coders, transmitters)
  • but about understanding captured data (better
    theory)

10
Status-quo video intelligence
  • Transport Canada CCTV Reference Manual for
    Security Application .
  • Australian Government National code of practice
    for CCTV applications in urban transport
  • USA Government recommended security Guidelines
    for Airport Planning, Design and Construction.
  • . refer to Motion-based capture as Intelligent
    Surveillance Technology, and make their
    recommendations based on thereon.

11
Motion-detection is not intelligent!
  • Term Motion-based is coined to make people
    believe that video recognition is happening,
    which is not!
  • Its actually illumination-change-based, as it
    uses simple point brightness comparison
  • Which often happens not because of motion!
  • Changing light / weather (esp. in 24/7
    monitoring)
  • Against sun/light, out of focus, blurred, thru
    glass
  • Reflections, diffraction, optical interferences
  • Image transmission, compression losses

12
Object-detection is intelligent
  • but few can do it, since necessary advances in
    video recognition theory became possible only
    recently (gt2002).
  • In 2002 National Research Council of Canada (NRC)
    starts developing Video Recognition Systems to
    leverage its scientific Video Recognition
    expertise for the industry.
  • In 2005, it develops ACE Surveillance
  • an object-detection-based Automated surveillanCE
    prototype capable of automatically extracting
    Annotated Critical Evidence from live video.
  • NRC becomes also the organizer of the first
    Canadian academic workshops dedicated to Video
    Processing for Security (since 2004)

13
What is ACE Surveillance?
  • A Windows software that performs real-time video
    analytics by integrating best object detection
    and tracking algorithms.
  • Replaces video clips with annotated still images
  • Compresses 1 Gb of video into 2 Mb of easy to
    browse and analyze still images

ACE Surveillance output A 7-hour activity from
day to night (1700 - 2400) is summarized into 2
minutes (600Kb) of Annotated Critical Evidence
snapshots. Note illumination changes! - Watch
tree shadows and sun light.
14
ACE Surveillance architecture
  • Works with ordinary USB cameras or CCTV cameras
    with USB video converters.
  • Adds on top of existing infrastructure using an
    ordinary desktop computer.

.
.
.
Video clips (Tb)
Archival mode of operation
Real-time mode of operation
15
Adds on top of existing infrastructure
16
Status quo Motion-based capture (Courtesy
NRC-IIT Video Recognition Systems project)
1. Many captured snapshots are useless either
noise or redundant 2. Without visual annotation,
motion information is lost. 3. Hourly
distribution of snapshots is not useful
17
ACE Surveillance Object-based
capture (Courtesy NRC Video Recognition Systems
project)
1. Each captured shot is useful. 2. Object
location and velocity shown augmentent. 3. Hourly
distribution of shots is indicative of what
happened in each hour, provides good
summarization of activities over long period of
time.
18
ACE Surveillance testing benchmarks
  • Tested in different levels of complexity
  • lighting conditions,
  • object motion patterns,
  • camera location
  • environmental constraints.
  • most difficult - outdoors in unconstrained
    environments with little or no object motion
    consistency (as around a private house in a
    regular neighbourhood).
  • most easy - in controlled indoor environment
    where minimal direct sunlight is present and
    where all objects are of approximately the same
    size and exhibit similar motion pattern (as at
    access gate inside the business building).

19
Outdoor, webcam, overview
Indoor with sunlight, CCTV
Indoor w/o sunlight, CCTV
Outdoor, wireless, eye-level
Camera / setup
Annotated CES
ACE daily summarization
20
Enables efficient detection of abnormal
activities
Delivery Entry
Back Door Entry
On week-day
On week-end
More than usual
21
ACE Surveillance results
  • In real-time mode alarm sounds last captured
    evidence (time-stamped) is shown.
  • In archival mode Zoom on the evidence browsing
    of captured evidences zoom on a day, on hour,
    then on event - point and click (for high res as
    needed)
  • Made Commissioners much more aware of activities.

22
Conclusions
  • Affordable automated (intelligent) video
    surveillance (AVS) is possible!
  • To replace traditional DVR
  • OR to supplement them DVR for 1 month AVS for
    1 year
  • However
  • Requires extra training from security officers.
  • Requires new protocols to handle automatically
    extracted evidence.
  • - From forensic prospective, data that are not
    original and have been processed by a computer
    can not be considered as evidence.
  • Requires new privacy policies.
  • - Surveillance data are normally not kept for a
    long period of time (lt1 month), due to their
    size. AVS allows to store on local machine many
    months (even years) of evidence data.

23
ACE surveillance case study outcome
  • ACE Surveillance (which is developed by a
    research lab) provides a reference standard
    against which can be measured solutions coming
    from industry.
  • It deals with common misconceptions related to
    deploying intelligent video surveillance systems
    (IVS)
  • motion detection myth vs real object detection
    and tracking.
  • The one-fit-all myth. - Extra video analytics
    expertise is required to set and operate IVS.
  • better video data (better resolution or
    compression) do not imply better video
    intelligence. - ACE Surveillance is shown to work
    with regular TV quality data (320 by 240 pixels).
  • However better quality of video image is needed
    for forensic purposes as evidence
  • Due to closing of the project by NRC, CBSA takes
    lead on it.
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