Finding missing persons by learning features for person attribute classification based on deep learning
First Claim
1. A method, comprising:
- identifying geographic locations of training images;
identifying weather information for each of the identified geographic locations;
generating image pairs from the training images;
for each image pair of the image pairs, determining whether images of the image pair include the same person; and
generating network parameters for a neural network with the identified geographic locations, the weather information for each of the identified geographic locations, and the determination of whether the images of the image pairs include the same person,wherein the identified geographic locations, identified weather information, and determination of whether the images of the image pair include the same person are obtained without human annotation.
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Accused Products
Abstract
An embodiment of the invention provides a method for finding missing persons by learning features for person attribute classification based on deep learning. A first component of a neural network identifies geographic locations of training images; and, a second component of the neural network identifies weather information for each of the identified geographic locations. A third component of the neural network generates image pairs from the training images. For each image pair of the image pairs, the third component of the neural network determines whether images of the image pair include the same person. The neural network generates neural network parameters with the identified geographic locations, the weather information for each of the identified geographic locations, and the determination of whether the images of the image pairs include the same person.
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Citations
19 Claims
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1. A method, comprising:
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identifying geographic locations of training images; identifying weather information for each of the identified geographic locations; generating image pairs from the training images; for each image pair of the image pairs, determining whether images of the image pair include the same person; and generating network parameters for a neural network with the identified geographic locations, the weather information for each of the identified geographic locations, and the determination of whether the images of the image pairs include the same person, wherein the identified geographic locations, identified weather information, and determination of whether the images of the image pair include the same person are obtained without human annotation. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A method for finding missing persons by learning features for person attribute classification based on deep learning, said method comprising:
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identifying geographic locations of training images by a first component of a neural network; identifying weather information for each of the identified geographic locations by a second component of the neural network; generating image pairs from the training images by a third component of the neural network, the image pairs being generated automatically by a video processing module, the video processing module including an object detector and tracker to detect and track people over a video sequence, and an image pair generator; for each image pair of the image pairs, determining whether images of the image pair include the same person by the third component of the neural network; and generating neural network parameters by the neural network, the neural network parameters being generated with the identified geographic locations, the weather information for each of the identified geographic locations, and the determination of whether the images of the image pairs include the same person, wherein the identified geographic locations, identified weather information, and determination of whether the images of the image pair include the same person are obtained without human annotation. - View Dependent Claims (8, 9, 10, 11, 12, 13, 14)
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15. A non-transitory computer-readable medium having computer-readable instructions stored thereon which when executed by a computer cause the computer to perform a method comprising:
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identifying geographic locations of training images; identifying weather information for each of the identified geographic locations; generating image pairs from the training images; for each image pair of the image pairs, determining whether images of the image pair include the same person; and generating network parameters for a neural network with the identified geographic locations, the weather information for each of the identified geographic locations, and the determination of whether the images of the image pairs include the same person, wherein the identified geographic locations, identified weather information, and determination of whether the images of the image pair include the same person are obtained without human annotation. - View Dependent Claims (16, 17, 18, 19)
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Specification