Method and apparatus for detecting abnormal movement
First Claim
1. An apparatus for detecting an abnormal movement, the apparatus comprising:
- at least one processor;
a feature tracing unit executed or controlled by the processor to extract features of a moving object in an input image, trace a variation in a position of the extracted features according to time, and ascertain trajectories of the extracted features;
a topic online learning unit executed or controlled by the processor to classify the input image in units of documents that are bundles of the trajectories expressed by a set of words representing positions of grid points through which the trajectories pass and a set of vector differences representing a set of differences in vectors between a position of an actual feature in a current frame and a position of an actual feature in a previous frame, and ascertain, by using an online learning method which is a probabilistic topic model, probability distribution states of topics included in a classified document; and
a movement pattern online learning unit executed or controlled by the processor to learn a velocity and a direction for each of the ascertained topics, and learn a movement pattern by inferring a spatiotemporal correlation between the ascertained topics by using a K-means clustering method.
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Abstract
Provided are a method and apparatus for detecting an abnormal movement. The apparatus includes a feature tracing unit configured to extract features of a moving object in an input image, trace a variation in position of the extracted features according to time, and ascertain trajectories of the extracted features; a topic online learning unit configured to classify the input image in units of documents which are bundles of the trajectories, and ascertain probability distribution states of topics, which constitute the classified document, by using an online learning method which is a probabilistic topic model; and a movement pattern online learning unit configured to learn a velocity and a direction for each of the ascertained topics, and learn a movement pattern by inferring a spatiotemporal correlation between the ascertained topics.
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Citations
21 Claims
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1. An apparatus for detecting an abnormal movement, the apparatus comprising:
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at least one processor; a feature tracing unit executed or controlled by the processor to extract features of a moving object in an input image, trace a variation in a position of the extracted features according to time, and ascertain trajectories of the extracted features; a topic online learning unit executed or controlled by the processor to classify the input image in units of documents that are bundles of the trajectories expressed by a set of words representing positions of grid points through which the trajectories pass and a set of vector differences representing a set of differences in vectors between a position of an actual feature in a current frame and a position of an actual feature in a previous frame, and ascertain, by using an online learning method which is a probabilistic topic model, probability distribution states of topics included in a classified document; and a movement pattern online learning unit executed or controlled by the processor to learn a velocity and a direction for each of the ascertained topics, and learn a movement pattern by inferring a spatiotemporal correlation between the ascertained topics by using a K-means clustering method. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. An apparatus for detecting an abnormal movement, the apparatus comprising:
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at least one processor; a feature tracing unit executed or controlled by the processor to extract features of a moving object in an input image, trace a variation in a position of the extracted features according to time, and ascertain trajectories of the extracted features; a trajectory classifying unit executed or controlled by the processor to classify the input image in units of documents indicating a bundle of the trajectories expressed by a set of words representing positions of grid points through which the trajectories pass and a set of vector differences representing a set of differences in vectors between a position of an actual feature in a current frame and a position of an actual feature in a previous frame, and infer, by using an online learning method which is a probabilistic topic model, a multinomial distribution parameter probability vector value indicating histogram distribution of topics constituting each document in order to cluster positions of the trajectories for each topic in the document; a spatiotemporal correlation inferring unit executed or controlled by the processor to infer a spatiotemporal correlation on the basis of the inferred multinomial distribution parameter probability vector value; and a movement pattern online learning unit executed or controlled by the processor to learn a velocity and a direction for each of the clustered topics, and learn a movement pattern by inferring a spatiotemporal correlation between the ascertained topics by using a K-means clustering method. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18)
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19. A method of detecting an abnormal movement, the method comprising:
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extracting, using a processor, features of a moving object in an input image; tracing a variation in position of the extracted features according to time; ascertaining trajectories of the extracted features; classifying the input image in units of documents that are bundles of the trajectories expressed by a set of words representing positions of grid points through which the trajectories pass and a set of vector differences representing a set of differences in vectors between a position of an actual feature in a current frame and a position of an actual feature in a previous frame; ascertaining probability distribution states of topics, which constitute a classified document, by using an online learning method which is a probabilistic topic model; learning a velocity and a direction for each of the ascertained topics; and learning a movement pattern by inferring a spatiotemporal correlation between the ascertained topics by using a K-means clustering method. - View Dependent Claims (20)
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21. A method of detecting an abnormal movement, the method comprising:
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extracting, using a processor, features of a moving object in an input image; tracing a variation in position of the extracted features according to time; ascertaining trajectories of the extracted features; classifying the input image in units of documents indicating a bundle of the trajectories expressed by a set of words representing positions of grid points through which the trajectories pass and a set of vector differences representing a set of differences in vectors between a position of an actual feature in a current frame and a position of an actual feature in a previous frame; inferring, by using an online learning method which is a probabilistic topic model, a multinomial distribution parameter probability vector value indicating histogram distribution of topics constituting each document in order to cluster positions of the trajectories for each topic in the document; inferring a spatiotemporal correlation on the basis of the inferred multinomial distribution parameter probability vector value; learning a velocity and a direction for each of the clustered topics; and learning a movement pattern by inferring a spatiotemporal correlation between the ascertained topics by using a K-means clustering method.
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Specification