Method and apparatus for detecting abnormal movement

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 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.

A method of detecting an abnormal movement, the method comprising:

• 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.

A method of detecting an abnormal movement, the method comprising:

• 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.