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Method and apparatus for machine vision classification and tracking
| Details |
Inventors: Brady, Mark J.; Cerny, Darin G.; Granholm, Michelle C.; Million, Belayneh W.;
Assignee: Minnesota Mining and Manufacturing Company (St. Paul, MN)
Primary Examiner: Couso; Yon J.
Assistant Examiner:
Attorney, Agent or Firm: Griswold; Gary L., Kirn; Walter N., Ouyang; Michael K.
A method and apparatus for classification and tracking objects in three-dimensional space is described. A machine vision system acquires images from roadway scenes and processes the images by analyzing the intensities of edge elements within the image. The system then applies fuzzy set theory to the location and angles of each pixel after the pixel intensities have been characterized by vectors. A neural network interprets the data created by the fuzzy set operators and classifies objects within the roadway scene. The system can also track objects within the roadway scene, such as vehicle, by forecasting potential track regions and then calculating match scores for each potential track region based on how well the edge elements from the target track regions match those from the source region as weighted by the extent the edge elements have moved. |
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DETAILED DESCRIPTION To overcome the limitations in the prior art described above, and to overcome other limitations that will become apparent upon reading and understanding the present specification, the present invention provides a method and apparatus for classifying and tracking objects in an image.
The method and apparatus disclosed can be utilized for classifying and tracking vehicles from a plurality of roadway sites, the images from the sites as provided in real-time by video cameras.
The images from the real-time video are then processed by an image processor which creates classification and tracking data in real-time and sends the data to some interfacing means.
The apparatus of the present invention includes a plurality of video cameras situated over a plurality of roadways, the video cameras filming the sites in real-time.
The video cameras are electrically interconnected to a switcher, which allows for manual or automatic switching between the plurality of video cameras.
The video is sent to a plurality of image processors which analyze the images from the video and create classification and tracking data.
The classification and tracking data may then be sent to a workstation, where a graphical user interface integrates the live video from one of the plurality of video cameras with traffic statistics, data and maps.
The graphical user interface further automatically displays alarm information when an incident has been detected.
The classification and tracking data may further be stored in databases for later use by traffic analysts or traffic control devices.
The present invention provides for a method for classifying vehicles in an image provided by real-time video.
The method first includes the step of determining the magnitude of vertical and horizontal edge element intensities for each pixel of the image.
Then, a vector with magnitude and angle is computed for each pixel from the horizontal and vertical edge element intensity data.
Fuzzy set theory is applied to the vectors in a region of interest to fuzzify the angle and location data, as weighted by the magnitude of the intensities
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