Adaptation for clear path detection using reliable local model updating
First Claim
1. A method for detecting a clear driving path for a vehicle, said method comprising:
- processing images from a camera to produce feature data for one or more patches from each of the images;
analyzing the feature data for the one or more patches using a trained classifier to determine a probability value;
selecting patches whose probability value exceeds a first probability threshold value as positive test samples;
selecting patches whose probability value does not exceed a second probability threshold value as negative test samples;
selecting patches whose probability value does not exceed a second probability threshold value as negative test samples;
using locations of the one or more patches to identify which training samples and which of the positive and negative test samples should be used to adaptively train the trained classifier, where the locations are defined in terms of feature vector space; and
making a clear-driving-path or non-clear-path determination for each of the one or more patches based on the probability value.
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Accused Products
Abstract
A method and system for vehicular clear path detection using adaptive machine learning techniques including reliable local model updating. Digital camera images are segmented into patches, from which characteristic features are extracted representing attributes such as color and texture. The patch features are analyzed by a Support Vector Machine (SVM) or other machine learning classifier, which has been previously trained to recognize clear path image regions. The SVM classifier is adaptively updated using reliable local test samples, such as positive clear path samples which just passed by the vehicle. The resultant classifier, being continuously and adaptively updated with recent, reliable training samples, exhibits improved performance and accuracy in analyzing subsequent image regions or patches for the existence of a clear driving path.
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Citations
18 Claims
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1. A method for detecting a clear driving path for a vehicle, said method comprising:
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processing images from a camera to produce feature data for one or more patches from each of the images; analyzing the feature data for the one or more patches using a trained classifier to determine a probability value; selecting patches whose probability value exceeds a first probability threshold value as positive test samples; selecting patches whose probability value does not exceed a second probability threshold value as negative test samples; selecting patches whose probability value does not exceed a second probability threshold value as negative test samples; using locations of the one or more patches to identify which training samples and which of the positive and negative test samples should be used to adaptively train the trained classifier, where the locations are defined in terms of feature vector space; and making a clear-driving-path or non-clear-path determination for each of the one or more patches based on the probability value. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A method for detecting a clear driving path for a vehicle, said method comprising:
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processing images from a camera to produce feature data for one or more patches from each of the images; analyzing the feature data for the one or more patches using a trained classifier to determine a probability value, where the trained classifier is a Support Vector Machine classifier with a Radial Basis Function kernel; selecting patches whose probability value exceeds a probability threshold value as positive test samples; using locations of the one or more patches to identify which training samples and which of the positive test samples should be used to adaptively train the trained classifier, where the locations are defined in terms of feature vector space; making a clear-driving-path or non-clear-path determination for each of the one or more patches based on the probability value; and using the clear-driving-path or non-clear-path determination in a driving assistance system in the vehicle. - View Dependent Claims (12, 13, 14)
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15. A clear path detection system for a vehicle comprising:
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a camera for providing images of a scene in front of the vehicle or behind the vehicle; a segmentation module for defining one or more patches from each of the images; a feature extraction module for extracting feature data for the one or more patches from each of the images; a classifier module for analyzing the feature data to determine a probability value for each of the one or more patches, where the classifier module uses a Support Vector Machine classifier with a Radial Basis Function kernel; a test sample retrieval module for using the feature data from patches whose probability value exceeds a probability threshold value, and which are local in feature vector space, as inputs to the Radial Basis Function kernel to adaptively train the classifier module; and a decision module for evaluating the probability value to make a clear-driving-path or non-clear-path determination for each of the one or more patches. - View Dependent Claims (16, 17, 18)
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Specification