Passive optical detection method and system for vehicles

Abstract

A system, method, and apparatus are discussed for a passive optical camera-based system to detect a presence of one or more vehicles with one or more cameras. A detection algorithm is applied to recognize of the presence of the one or more vehicles using one or more imaging processors and the one or more cameras to detect fluctuations in light intensity from scattered light and/or reflections off of that vehicle. Those scattered light and/or reflections are captured in images contained in a set of frames from the one or more cameras.

1 - 20 . (canceled) 
     
     
         21 . A system, comprising:
 a passive optical camera-based system configured to detect a presence of one or more vehicles with one or more cameras, where the passive optical camera-based system also has a detection algorithm to recognize the presence of the one or more vehicles using one or more imaging processors, where the detection algorithm is further configured to detect the one or more vehicles captured in a set of frames by the one or more cameras via a fluctuation signature that is based on a frequency of at least one of i) a moving part of the given vehicle and ii) a vibration of a part of a given vehicle; and   where any portions of the detection algorithm implemented in software are stored in an executable format in a memory and are executed by the one or more imaging processors.   
     
     
         22 . The system of  claim 21 , further comprising:
 a database to store known characteristics of possible vehicles including the frequency of the vibration of the part and/or the frequency of the moving part associated with vehicles, where the detection algorithm is configured to use the database to recognize the fluctuation signature of possible vehicle candidates to confirm the presence of the one or more vehicles captured in the set of frames by the one or more cameras.   
     
     
         23 . The system of  claim 22 , wherein the detection algorithm is further configured to use the database to further classify a type of vehicle captured in the set of frames by the one or more cameras based in part on an expected possible frequency band of operation of that type of vehicle. 
     
     
         24 . The system of  claim 22 , wherein the detection algorithm is further configured to additionally use i) a maneuver pattern, ii) a visual appearance, and ii) any combination of the maneuver pattern and the visual appearance, of possible vehicle candidates, to recognize the fluctuation signature of possible vehicle candidates to confirm the presence of the one or more vehicles captured in the set of frames by the one or more cameras. 
     
     
         25 . The system of  claim 21 , wherein the detection algorithm is further configured to recognize the presence of a first vehicle that is hovering via the frequency of at least one of i) the moving part of the given vehicle and ii) the vibration of the part of the given vehicle without a need to perceive the first vehicle to be moving vertically or laterally with respect to one or more of the cameras. 
     
     
         26 . The system of  claim 21 , wherein the detection algorithm and the image processors are further configured to detect frequency information for the vibration and/or moving part of the given vehicle by measuring a light modulation caused by surface vibrations or moving mechanical features of that vehicle. 
     
     
         27 . The system of  claim 21 , wherein the detection algorithm and its cooperation with a database is further configured to pick out two or more different fluctuation signatures, via a plurality of fluctuation signatures contained in the database, from the one or more vehicles captured in the set of frames. 
     
     
         28 . The system of  claim 21 , wherein the detection algorithm and the image processors are further configured to spatially locate and separate a frequency content of time-varying light sources in the set of frames; and therefore, enable the detection of i) the frequency of i) the moving part and/or ii) the vibration of the part of possible vehicle candidates captured in the set of frames by the one or more cameras. 
     
     
         29 . The system of  claim 21 , wherein the detection algorithm is further configured to use a Fast Fourier transform (FFT), on the set of frames to convert fluctuations in light intensity over time to a frequency domain along a time axis of camera imagery captured by the one or more cameras in order to detect i) light fluctuations caused by moving mechanical features of that given vehicle and/or ii) vibration of other parts of the given vehicle. 
     
     
         30 . The system of  claim 21 , further comprising:
 a software-based real-time digital image stabilization application that is configured to i) combine data from an inertial measurement unit that measures a motion of a first camera and ii) then generates motion compensation data to correct data captured in the set of frames in real time before image processing occurs, where the inertial measurement unit is configured to track camera pointing direction, where the digital image stabilization application and the inertial measurement unit are configured to enable the one or more cameras mounted on a moving or vibrating platform to prevent a motion of the cameras from interfering with the detection algorithm using the fluctuation signature that is based on the frequency of the vibration of the part and/or frequency of the moving part of the given vehicle.   
     
     
         31 . A method for a passive optical camera-based method, comprising:
 detecting a presence of one or more vehicles with one or more cameras in the passive optical camera-based method; and   applying a detection algorithm to recognize of the presence of the one or more vehicles using one or more imaging processors and the one or more cameras, where the detection algorithm is further configured to detect the one or more vehicles captured in a set of frames by the one or more cameras via a fluctuation signature that is based on a frequency of at least one of i) a moving part of the given vehicle and ii) a vibration of a part of a given vehicle.   
     
     
         32 . The method of  claim 31 , further comprising:
 using a database to store known characteristics of possible vehicles including the frequency of the vibration of the part and/or the frequency of the moving part associated with vehicles, where the detection algorithm is configured to use the database to recognize the fluctuation signature of possible vehicle candidates to confirm the presence of the one or more vehicles captured in the set of frames by the one or more cameras.   
     
     
         33 . The method of  claim 32 , further comprising:
 using the detection algorithm and its cooperation with the database to further classify a type of vehicle captured in the set of frames by the one or more cameras based in part on an expected possible frequency band of operation of that type of vehicle.   
     
     
         34 . The method of  claim 32 , wherein the detection algorithm additionally uses i) a maneuver pattern, ii) a visual appearance, and ii) any combination of the maneuver pattern and the visual appearance, of possible vehicle candidates, to recognize the fluctuation signature of possible vehicle candidates to confirm the presence of the one or more vehicles captured in the set of frames by the one or more cameras. 
     
     
         35 . The method of  claim 31 , further comprising:
 using the detection algorithm to recognize the presence of a first vehicle that is hovering via the frequency of at least one of i) the moving part of the given vehicle and ii) the vibration of the part of the given vehicle without a need to perceive the first vehicle to be moving vertically or laterally with respect to one or more of the cameras.   
     
     
         36 . The method of  claim 31 , further comprising:
 using the detection algorithm and its cooperation with the image processors to detect frequency information for the vibration or moving part of the given vehicle by measuring a light modulation caused by surface vibrations or moving mechanical features of that vehicle.   
     
     
         37 . The method of  claim 31 , further comprising:
 using the detection algorithm and its cooperation with a database to further pick out two or more different fluctuation signatures, via a plurality of fluctuation signatures contained in the database, from the one or more vehicles captured in the set of frames.   
     
     
         38 . The method of  claim 31 , further comprising:
 using the detection algorithm and its cooperation with the image processors to spatially locate and separate a frequency content of time-varying light sources in the set of frames; and therefore, enable the detection of the frequency of i) the moving part and/or ii) the vibration of the part of possible vehicle candidates captured in the set of frames by the one or more cameras.   
     
     
         39 . The method of  claim 31 , wherein the detection algorithm is further configured to use a Fast Fourier transform (FFT), on the set of frames to convert fluctuations in light intensity over time to a frequency domain along a time axis of camera imagery captured by the one or more cameras in order to detect i) light fluctuations caused by moving mechanical features of that given vehicle and/or ii) vibration of other parts of the given vehicle. 
     
     
         40 . The method of  claim 31 , further comprising:
 using a software-based real-time digital image stabilization application that i) combines data from an inertial measurement unit that measures a motion of a first camera and ii) then generates motion compensation data to correct data captured in the set of frames in real time before image processing occurs, where the inertial measurement unit is configured to track camera pointing direction, where the digital image stabilization application and the inertial measurement unit are configured to enable the one or more cameras mounted on a moving or vibrating platform to prevent a motion of the cameras from interfering with the detection algorithm using the fluctuation signature that is based on the frequency of the vibration of the part and/or frequency of the moving part of the given vehicle.