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I A I Infrared Security System and Method US Patent 7,738,008 June 15 2010
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Transcript of I A I Infrared Security System and Method US Patent 7,738,008 June 15 2010
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IAIInfrared Security System and Method
US Patent 7,738,008 June 15 2010
How Does It Work?
June 2010
IAI = Infrared Applications Inc.
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Test Set-up: Visual Orientation
• Two cameras with a common surveillance field of view.
• Camera B can be seen in Camera A’s FOV.
• Camera A is positioned in Camera B’s FOV
• Angles between cameras & targets are as shown
• The Cameras are 105 feet apart.
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Camera A
Target
Camera B Location
Camera A Angle
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Camera B
Camera A Location
Target
Camera B angle
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ISS Geometry
Location Camera A
Location Camera B
Target Planter Location
Distance Between Cameras (105 feet)
R2R1
Set up value, distance between CamerasCameras & target Actual PositionsRange computations, R1 & R2
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Real time calculation of Target Size
• Cameras are IR calibrated and balancedGain & Level using common objects
• Each IR camera employs convention 2 dimensional processing.
Target segmentationThreshold
• Two dimensional information is processed in real time into 3 dimensional information
Precise object location (x,y,z, coordinates)Precise physical size (sq. ft)
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Threat Determination
• Targets are defined by size – (eg: truck, car, large animal, human, small
animal/child, very small animal)
• A threat is defined as a specific target in a defined location
• The Location– All or part of surveillance field – Or a specific threat area: No fly zone
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Target Upgrade & Tracking
• Target was defined by actual size. • Once classified as a threat
– Actual the actual target size is stored.– Actual Inherent Thermal Contrast is stored.
• Threat is continuously tracked by:– Both cameras or one camera if either camera
becomes obscured. (2 D tracking using Actual target size for ranging and Inherent contrast for improved discrimination)
• Continuously tracking: – Allows higher order threat determinations
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Intruder Example
• Series of snap shots of an intruder• The initial detection is by Camera A.• Then, Intruder enters Camera B FOV• The intruder enters the yard.• The intruder is continuously tracked
through partial and total camera obscurations.
• Snap shots are 1/40 the actual number of independent samples at video frame rates.
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Camera B, initial detection
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Camera A ------ Camera B
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Camera A ------ Camera B Almost fully obscured
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Camera A ------ Camera B
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Camera A ------ Camera B
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Camera A ------ Camera B
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Camera A ------ Camera B Partially Obscured Mostly Obscured
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Intruder
• The target alarm sounded approximately 0.5 seconds after Camera B detection.
• The highly cluttered scene caused each camera to lose the target because of complete or partially obscurations.
• The “arc” path of the intruder causes an aspect change with small changes in computed size.
• With more than 300 independent sampled image pairs, the confidence level is extremely high.
• The Intruder was observed to be carrying a tool or a weapon.
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Object in Hand
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Advance Discrimination Techniques
• Target Refinement: – ITC & Size of each target allows discrimination between targets in a
multi-target environment.• Target Image Dropout
– “Inherent thermal contrast” and actual size are used to re-acquire and separate new targets from old target.
• Multiple targets: – Can merge together and then separate, where ITC and physical size
assigned to each target are used to maintain the identity of each target. • Behavioral traits
– Movement over time against a preset criteria are associated with a certain kinds of threat.
• Redundant information– ITC and physical size provides redundant data that support the
application of best estimate theory.
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Advance Discrimination Techniques
• Designed for multiple targets, each target having a separate threat definition, and threat response. (examples one or more)– “People-size” targets in specific areas at defined times– People congregating (crowd recognition)– Loitering (excessive time)– Stalking, (time history relationship between two targets)– Lying in wait, (serious home evasion threat)– A unattended child entering a swimming pool– Animals entering controlled areas– People exhibiting threatening behavioral – “Man down” recognition– Cars, time and location criteria– Trucks, time and location criteria– People count, matching entering with exits, tagging size– Verification, matching size with independent data, e.g. RFID data
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Summary
• Field tests have demonstrated the attributes of the Infrared Security System.
• ISS provides reliable target detection and threat classification.
• High level of confidence that all false alarms have been rejected or minimized.
• ISS has the capability to be the first fully automatic physical security system
• ISS minimizes or eliminates the costly dedicated control rooms of TV monitors and security analysts.
• ISS provides the real time information needed by a first responder, or Information needed by the occupants of the home to avoid the threat.
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ISS Applications• Major Business Sectors
– Home Security (adjunct to existing home security)– Factories (upgrade from forensic to threat negation)– 24/7 High Value (integrated threat assessment)
• Power Plants• Refineries• Farms (man and environmental threats, seasonal threat) • Military Installations & portable field operations• Shopping Malls, parking lot security (host of threats)• Airports: intrusion, unattended luggage, & threat tracking
• Green Applications– Automobile Sales lots– Correctional Institutions– Transportation Depots/shipyards/docks
• Other forms employing the core patented principle of 3D processing