I A I Infrared Security System and Method US Patent 7,738,008 June 15 2010

1. IAIInfrared Security System and MethodUS Patent 7,738,008 June 15 2010 How Does It Work? June 2010 IAI = Infrared Applications Inc.

2. 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.

3. Camera A Camera B Location Camera A Angle Target

4. Camera B Camera A Location Camera B angle Target

5. ISS Geometry Location Camera B Distance Between Cameras (105 feet) Location Camera A R2 R1 Target Planter Location Set up value, distance between Cameras Cameras & target Actual Positions Range computations, R1 & R2

6. Real time calculation of Target Size • Cameras are IR calibrated and balanced Gain & Level using common objects • Each IR camera employs convention 2 dimensional processing. Target segmentation Threshold • Two dimensional information is processed in real time into 3 dimensional information Precise object location (x,y,z, coordinates) Precise physical size (sq. ft)

7. 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

8. 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

9. 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.

10. Camera B, initial detection

11. Camera A ------ Camera B

12. Camera A ------ Camera B Almost fully obscured

13. Camera A ------ Camera B

14. Camera A ------ Camera B

15. Camera A ------ Camera B

17. Camera A ------ Camera B Partially Obscured Mostly Obscured

18. 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.

19. Object in Hand

20. 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.

21. 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

22. 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.

23. 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