Human and object recognition in digital video
First Claim
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1. A human and object recognition and tracking video image processing tool comprising the computer implemented steps of:
- (a) obtaining a stream of color video images in the YUV color space;
(b) comparing a current video image in the stream to a reference image generated as a background model from past video images in the stream;
(c) determining a foreground image by using a mask to ignore each current region from the current video image which satisfies any of the following tests in relation to correspondingly positioned regions of the reference image;
(i) an intensity difference value generated from a neighbourhood of the current region and neighbourhoods of the corresponding region of the reference image is less than a first threshold;
or (ii) the intensity difference is between the first threshold and a second threshold, a texture difference generated from a shadow filter neighbourhood of the current region and a shadow filter neighbourhood of the corresponding region of the reference image is zero, a color difference generated from a shadow filter neighbourhood of the current region and a shadow filter neighbourhood of the corresponding region of the reference image is less than a color difference threshold, and the current region is darker than the corresponding region of the reference image.
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Abstract
The current invention is a method or a computer implemented tool for robust, low CPU, low resolution human tracking which may be implemented a part of a digital video management and surveillance system or on a digital video recorder. The method involves use of intensity, texture and shadow filtering in the YUV color space to reduce the number of false objects detected. The thresholds for background segmentation may be dynamically adjusted to image intensity. The human and object recognition feature operates on an adaptive codebook based learning algorithm.
224 Citations
18 Claims
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1. A human and object recognition and tracking video image processing tool comprising the computer implemented steps of:
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(a) obtaining a stream of color video images in the YUV color space;
(b) comparing a current video image in the stream to a reference image generated as a background model from past video images in the stream;
(c) determining a foreground image by using a mask to ignore each current region from the current video image which satisfies any of the following tests in relation to correspondingly positioned regions of the reference image;
(i) an intensity difference value generated from a neighbourhood of the current region and neighbourhoods of the corresponding region of the reference image is less than a first threshold;
or(ii) the intensity difference is between the first threshold and a second threshold, a texture difference generated from a shadow filter neighbourhood of the current region and a shadow filter neighbourhood of the corresponding region of the reference image is zero, a color difference generated from a shadow filter neighbourhood of the current region and a shadow filter neighbourhood of the corresponding region of the reference image is less than a color difference threshold, and the current region is darker than the corresponding region of the reference image. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A method for recognizing objects within a field of view of a digital video camera comprising the steps of,
(a) obtaining a stream of color video images in the YUV color space; -
(b) generating a reference image as a background model from past video images in the stream;
(c) determining a foreground image by comparing a current image from the stream of color video images to the reference image using a shadow filter;
(d) segmenting blobs in the foreground image;
(e) generating a feature vector for each blob;
(f) computing a vector difference between each feature vector to a codebook of code vectors, to determine a closest match code vector; and
(g) recognizing the blob as the closest match code vector if the vector difference is less than a match threshold. - View Dependent Claims (14, 15, 16, 17, 18)
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Specification