COUNTER SURVEILLANCE SYSTEM

• US 20130093894A1
• Filed: 05/01/2012
• Published: 04/18/2013
• Est. Priority Date: 05/18/2011
• Status: Abandoned Application

• Current Assignee: Andrey Zlotnikov
• Original Assignee: Andrey Zlotnikov
• Inventors: Zlotnikov, Andrey
• Application Number:
  US13/805,870
• Publication Number:
  US 20130093894A1
• Time in Patent Office: Days
• US Class Current: 348/148
• CPC Class Codes: G08G 1/0175 by photographing vehicles, e.g. when violating traffic rules
• CPC Class Codes: H04N 7/18 Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast

Abstract

Vehicle mounted cameras are used as counter surveillance devices to detect surveillance of a protected vehicle. These cameras are located in forward and rearward looking locations in the protected vehicle and are in communication with vision processing software that a.) identifies license tags of surrounding vehicles, which are then run through various data-bases of friends and foes, and b.) records surrounding vehicle movements in an attempt to identify trailing vehicles that (for instance) remain within a certain distance of the protected vehicle for long periods of time, or fit other criteria.

Claims

1. A method of surveillance detection comprising steps of:

a. providing a set of detecting means;

b. providing computing means in communication with said detectors adapted to analyze data from said set of detectors;

c. comparing said data with a database of known surveillance characteristics;

d. determining if the detected data falls into any category of known surveillance activity.

2. The method of claim 1 further issuing an alert upon determining that detected data falls into a category of known surveillance activity.

3. The method of claim 1 wherein said database comprises vehicle identification numbers.

4. The method of claim 1 wherein said known surveillance characteristics include movements of vehicles.

5. The method of claim 1 wherein said known surveillance characteristics include movements of humans.

6. The method of claim 1 wherein said detectors are selected from the group consisting of:

video cameras;

radio receivers;

optical spectrometers;

mass spectrometers;

near field monitors;

load analyzers;

spectrum analyzers;

vibration detectors;

camera detectors;

wireless detectors;

radar.

7. The method of claim 1 wherein said detectors and computing means are hidden within a vehicle.

8. The method of claim 1 wherein said database includes information selected from the group consisting of:

vehicle surveillance patterns;

vehicle identification characteristics;

face data;

distinguishing mark data;

surveillance equipment frequencies;

mass spectra;

optical spectra;

power spectra.

9. The method of claim 1 wherein said step of determining if detected data falls into any category of known surveillance activity is accomplished by means of an algorithm selected from the group consisting of:

gradient descent, simplex, convex minimization, neural networks, Bayesian networks, support vector machine, linear programming methods, nonlinear programming methods, Hessian methods, gradient methods, thermodynamic methods, entropic methods, and simulated annealing.

10. The method of claim 1 where said computing means comprises vision processing means;

audio processing means;

spectrum analysis means;

pattern detection means.

11. The method of claim 1 wherein said database contains data selected from the group consisting of:

stolen vehicle identification data;

hostile entity identification data;

criminal element identification data.

12. A system for surveillance detection comprising:

a. a set of detecting means;

b. computing means in communication with said detectors adapted to analyze data from said set of detectors;

c. a database of known surveillance characteristics;

d. means for determining if detected data falls into any category of known surveillance activity by comparing said data to said database.

13. The system of claim 12 further issuing an alert upon determining that detected data falls into a category of known surveillance activity.

14. The system of claim 12 wherein said database comprises vehicle identification numbers.

15. The system of claim 12 wherein said known surveillance characteristics include movements of vehicles.

16. The system of claim 12 wherein said known surveillance characteristics include movements of humans.

17. The system of claim 12 wherein said detectors are selected from the group consisting of:

video cameras;

radio receivers;

optical spectrometers;

mass spectrometers;

near field monitors;

load analyzers;

spectrum analyzers;

vibration detectors;

camera detectors;

wireless detectors;

radar.

18. The system of claim 12 wherein said detectors and computing means are hidden within a vehicle.

19. The system of claim 12 wherein said database includes information selected from the group consisting of:

vehicle surveillance patterns;

vehicle identification characteristics;

face data;

distinguishing mark data;

surveillance equipment frequencies;

mass spectra;

optical spectra;

power spectra.

20. The system of claim 12 wherein said step of determining if detected data falls into any category of known surveillance activity is accomplished by means of an algorithm selected from the group consisting of:

gradient descent, simplex, convex minimization, neural networks, Bayesian networks, support vector machine, linear programming methods, nonlinear programming methods, Hessian methods, gradient methods, thermodynamic methods, entropic methods, and simulated annealing.

21. The system of claim 12 where said computing means comprises vision processing means;

audio processing means;

spectrum analysis means;

pattern detection means.

22. The system of claim 12 wherein said database contains data selected from the group consisting of:

stolen vehicle identification data;

hostile entity identification data;

criminal element identification data.