Method and apparatus for detecting abnormal movement

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.

2. The apparatus of claim 1 , further comprising an abnormality detecting unit configured to classify the movement pattern, in response to the trajectories comprised in each frame of the input image having a low probability of being included in the learned movement pattern, as an abnormal movement pattern by using a Gaussian learning result of the learned movement pattern.

3. The apparatus of claim 1 , wherein the feature tracing unit is configured to extract features of a moving object in an input image by using a Kanade-Lucas-Tomasi (KLT) tracing technique.

4. The apparatus of claim 1 , wherein in the topic online learning unit, the document includes a plurality of topics which are generated by multinomial distribution, and a multinomial distribution parameter probability vector value indicating a probability distribution state of the plurality of topics constituting the document is inferred to cluster positions of the trajectories for each topic.

5. The apparatus of claim 1 , wherein the multinomial distribution parameter probability vector value indicating a probability distribution state of the plurality of topics constituting the document comprises probability distribution (θ d ) and topic-word probability distribution (φ k ).

6. The apparatus of claim 1 , wherein the trajectory is expressed by a set of words w ji and a set of vector differences v jiT , and

wherein w ji indicates that a j-th trajectory passes through an i-th grid, and the set of vector differences represents a set of differences v jiT in vectors between a position of an actual feature in the word and a position of the actual feature before T frames.

7. The apparatus of claim 6 , wherein the word w ji is based on multinomial distribution, and the difference v jiτ in vectors comprised in the words has Gaussian distribution.

8. The apparatus of claim 6 , wherein a velocity is ascertained for each work position by using a position value of a current frame of a specific grid and an actual position value of the specific grid before τ frames.

9. The apparatus of claim 1 , wherein the online learning method is an online latent dirichlet distribution (OLDA) learning method.

10. The apparatus of claim 9 , wherein in the OLDA learning method, LDA is inferred using variational bayes (VB).

11. 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.

12. The apparatus of claim 11 , further comprising an abnormality detecting unit configured to classify the movement pattern, in response to the trajectories comprised in each frame of the input image having a low probability of being comprised in the learned movement pattern, as an abnormal movement pattern by using a Gaussian learning result of the learned movement pattern.

13. The apparatus of claim 11 , wherein the multinomial distribution parameter probability vector value comprises probability distribution (θ d ) and topic-word probability distribution (φ k ).

14. The apparatus of claim 11 , wherein the trajectory is expressed by a set of words w ji and a set of vector differences v jiT , and

wherein w ji indicates that a j-th trajectory passes through an i-th grid, and the set of vector differences represents a set of differences v jiT in vector between a position of an actual feature in the word and a position of the actual feature before T frames.

15. The apparatus of claim 14 , wherein the word w ji is based on multinomial distribution, and the difference v jiτ in vectors comprised in the words has Gaussian distribution.

16. The apparatus of claim 14 , wherein a velocity is ascertained for each work position by using a position value of a current frame of a specific grid and an actual position value of the specific grid before τ frames.

17. The apparatus of claim 11 , wherein the feature tracing unit is configured to extract features of the moving object in the input image by using a Kanade-Lucas-Tomasi (KLT) tracing technique.

18. The apparatus of claim 11 , wherein the online learning method is an online latent dirichlet distribution (OLDA) learning method.

19. 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.

20. The method of claim 19 , further comprising classifying the moment pattern, in response to the trajectories comprised in each frame of the input image having a low probability of being comprised in the learned movement pattern, as an abnormal movement pattern by using a Gaussian learning result of the learned movement pattern.

21. 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.