Communications: electrical – Vehicle detectors – Speed and overspeed
Reexamination Certificate
2001-06-12
2004-02-24
Tweel, John (Department: 2632)
Communications: electrical
Vehicle detectors
Speed and overspeed
C340S937000, C340S933000
Reexamination Certificate
active
06696978
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates generally to laser and radar speed detectors, and more particularly, to laser and radar speed detectors having a camera for capturing image data of a license plate and other identifying data of an automobile.
2. Prior Art
Conventional laser/radar speed violation detectors used by law enforcement officials do not use any form of visual input for vehicle identification. This problem becomes specifically acute when the laser/radar gun is pointed at one vehicle but there are other vehicles around. In such a case, not only can the police make a wrong decision, but a defendant can use legal means to repudiate the law enforcement officer's evidence.
It is known in the art to capture bar code image data positioned on the license plate of a car in order to identify the car. For instance, if the car is speeding, a summons can be sent to the car's owner. However, unless the camera is well positioned directly in view of the bar-code, such an image is very difficult to obtain.
SUMMARY OF THE INVENTION
Therefore it is an object of the present invention to provide a device for identifying vehicles traveling in excess of a predetermined speed limit which overcomes the disadvantages of the prior art.
It is another object of the present intention to provide a device for identifying vehicles traveling in excess of a predetermined speed limit which easily obtains identifying data from a target vehicle under less than perfect conditions.
It is still a further object of the present invention to provide a device for identifying vehicles traveling in excess of a predetermined speed limit which is difficult to repudiate by an offender.
Accordingly, a device for identifying vehicles traveling in excess of a predetermined speed is provided. The device comprises: speed measurement means for measuring the speed of a target vehicle and outputting a signal indicative of the measured speed; a camera operatively connected to the speed measurement means for capturing image data of a license plate affixed to the target vehicle when the target vehicle exceeds the predetermined speed; and tagging means for tagging the captured image data with the signal indicative of the measured speed of the target vehicle.
The device preferably further comprises: a recognition system for recognizing the license plate of the target vehicle; and at least one of a pan, tilt, and zoom motors operatively connected to the camera and controlled by the recognition system to provide an enhanced view of the license plate.
More preferably, the device further comprises classification means for classifying the type of target vehicle and outputting a signal indicative of the type of target vehicle, wherein the tagging means further tags the captured image data with the signal indicative of the type of target vehicle.
Still more preferably, the device further comprises color distribution identification means for identifying a color distribution of the target vehicle and outputting a signal indicative of the color distribution of the target vehicle, wherein the tagging means further tags the captured image data with the signal indicative of the color distribution of the target vehicle.
The tagging means preferably comprises: conversion means for converting the signals indicative of the measured speed of the target vehicle, the type of the target vehicle, and/or the color distribution of the target vehicle into alpha-numeric data; and combining means for combining the image data and the alpha numeric data. The device preferably further comprises transmitting means for transmitting the tagged image data, such as to other law enforcement vehicles within a predetermined distance.
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Raja, et al., “Segmentation and Tracking Using Colour Mixture Models”, Computer Vision—ACCV'98, Lecture Notes in Computer Science 1351.
Colmenarez Antonio J.
Gutta Srinivas
Trajkovic Miroslav
Thorne Gregory L.
Tweel John
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