Context adaptive approach in vehicle detection under various visibility conditions
First Claim
1. A method for adaptive detection of an object in an image, by using a plurality of clusters, each of the plurality of clusters being characterized by a range of values of one or more statistical parameters associated with a prior image, each cluster being part of a category, a category being part of a plurality of categories, comprising:
- receiving the image;
determining a value for each of the one or more statistical parameters of the image;
assigning the image to one of the plurality of clusters according to the determined value of each of the one or more statistical parameters of the image;
selecting a classifier for detecting the object based on the category associated with the assigned one of the plurality of clusters; and
detecting the object using the classifier.
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Abstract
Adaptive vision-based vehicle detection methods, taking into account the lighting context of the images are disclosed. The methods categorize the scenes according to their lighting conditions and switch between specialized classifiers for different scene contexts. Four categories of lighting conditions have been identified using a clustering algorithm in the space of image histograms: Daylight, Low Light, Night, and Saturation. Trained classifiers are used for both Daylight and Low Light categories, and a tail-light detector is used for the Night category. Improved detection performance by using the provided context-adaptive methods is demonstrated. A night time detector is also disclosed.
71 Citations
21 Claims
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1. A method for adaptive detection of an object in an image, by using a plurality of clusters, each of the plurality of clusters being characterized by a range of values of one or more statistical parameters associated with a prior image, each cluster being part of a category, a category being part of a plurality of categories, comprising:
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receiving the image;
determining a value for each of the one or more statistical parameters of the image;
assigning the image to one of the plurality of clusters according to the determined value of each of the one or more statistical parameters of the image;
selecting a classifier for detecting the object based on the category associated with the assigned one of the plurality of clusters; and
detecting the object using the classifier. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A system for adaptive detection of an object in an image by using a plurality of clusters, each of the plurality of clusters being characterized by a range of values of one or more statistical parameters associated with a prior image, each cluster being part of a category, a category being part of a plurality of categories, comprising:
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a processor;
software operable on the processor to;
receiving the image;
determining a value for each of the one or more statistical parameters of the image;
assigning the image to one of the plurality of clusters according to the determined value of each of the one or more statistical parameters of the image;
selecting a classifier for detecting the object based on the category associated with the assigned one of the plurality of clusters; and
detecting the object using the classifier. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20, 21)
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Specification