Methods and apparatuses for detecting anomalies in the compressed sensing domain

1. A method for processing a source signal to detect anomalies, the method comprising:

receiving, using at least one processor, a measurement vector of compressive measurements, the measurement vector being derived by applying a sensing matrix to the source signal;

generating, using the at least one processor, at least one first feature vector from the measurement vector, the first feature vector being an estimate of a second feature vector, the second feature vector corresponding to a translation of the source signal, the generating including generating more than one first feature vectors, each of the first feature vectors having a corresponding translation parameter;

comparing, using the at least one processor, a group of elements of a set of the first feature vectors with other elements of the set of first feature vectors;

determining, using the at least one processor, which elements in the group of the set of the first feature vectors are similar to the other elements of the set of first feature vectors; and

detecting, using the at least one processor, an anomaly in the source signal based on the first feature vector.

2. The method of claim 1 , wherein generating the at least one first feature vector is done without reconstructing the source signal.

3. The method of claim 1 , wherein the generating includes computing linear combinations of the at least one first feature vector.

4. The method of claim 1 , wherein the sensing matrix is derived from a circulant matrix such that the sensing matrix contains a row that is obtained by shifting a different row in the sensing matrix.

5. The method of claim 1 , wherein the source signal is multi-dimensional and the translation parameter includes translations in one or more dimensions.

6. The method of claim 5 , wherein the source signal is obtained from one or more image frames.

7. The method of claim 6 , further comprising:

performing, using the at least one processor, edge detection by generating a linear combination of the at least one first feature vector,

the performing corresponding to subtracting a linear combination of neighboring pixels from pixels in the source signal.

8. The method of claim 6 , wherein the detecting an anomaly includes detecting motion in the one or more image frames.

9. The method of claim 8 , wherein more than one first feature vectors are generated, each of the first feature vectors having a corresponding translation parameter, the method further comprising:

comparing, using the at least one processor, a group of elements of a set of the first feature vectors with other elements of the set of first feature vectors, and

determining, using the at least one processor, which elements in the group of the set of the first feature vectors are similar to the other elements of the set of first feature vectors.

10. The method of claim 9 , further comprising:

determining, using the at least one processor, at least one of a speed of the detected motion, a direction of the detected motion, and a certainty of the detected motion,

the determining the at least one of the speed of the detected motion, the direction of the detected motion, and the certainty of the detected motion being based on at least one of (i) the translation parameters associated with the elements determined to be similar to each other, and (ii) a degree of similarity of the elements determined to be similar to each other.

11. The method of claim 8 , wherein the detected motion triggers an action if the detected motion meets at least one criterion.

12. A method for obtaining a measurement vector for processing a source signal to detect anomalies, the method comprising:

generating, using at least one processor, a measurement vector by applying a sensing matrix to the source signal, the sensing matrix being a shift-preserving sensing matrix, the measurement vector being used to detect anomalies in the source signal, wherein

more than one first feature vectors is generated from the measurement vector, the first feature vector being an estimate of a second feature vector, the second feature vector corresponding to a translation of the source signal, each of the first feature vectors having a corresponding translation parameter,

a group of elements of a set of the first feature vectors are compared with other elements of the set of first feature vectors, and

elements in the group of the set of the first feature vectors are analyzed to determine which are similar to the other elements of the set of first feature vectors.

13. The method of claim 12 , wherein the source signal is multi-dimensional.

14. The method of claim 13 , wherein the source signal is obtained from one or more images.

15. The method of claim 14 , wherein one or more images are derived by applying a windowing function to one or more original images.

16. The method of claim 14 , wherein the one or more images are a video signal.

17. A server for processing a source signal to detect anomalies, the server configured to:

receive a measurement vector of compressive measurements, the measurement vector being derived by applying a sensing matrix to a source signal;

generate at least one first feature vector from the measurement vector, the first feature vector being an estimate of a second feature vector, the second feature vector corresponding to a translation of the source signal, the generating including generating more than one first feature vectors, each of the first feature vectors having a corresponding translation parameter;

compare a group of elements of a set of the first feature vectors with other elements of the set of first feature vectors;

determine which elements in the group of the set of the first feature vectors are similar to the other elements of the set of first feature vectors; and

detect an anomaly in the source signal based on the first feature vector.

18. A image detection device for obtaining a measurement vector for processing a source signal to detect anomalies, the image detection device configured to:

generate a set of compressive measurements by applying a sensing matrix to a source signal, the sensing matrix being a shift-preserving sensing matrix; and

generate a measurement vector based on the set of compressive measurements, the measurement vector being used to detect anomalies in the source signal,

wherein

more than one first feature vectors is generated from the measurement vector, the first feature vector being an estimate of a second feature vector, the second feature vector corresponding to a translation of the source signal, each of the first feature vectors having a corresponding translation parameter,

a group of elements of a set of the first feature vectors are compared with other elements of the set of first feature vectors, and

elements in the group of the set of the first feature vectors are analyzed to determine which are similar to the other elements of the set of first feature vectors.

19. An image detection system for detecting anomalies in a source signal, the system comprising:

an image detection device for obtaining a measurement vector for processing the source signal, the image detection device configured to,

generate a set of compressive measurements by applying a shift-preserving sensing matrix to a source signal, and

generate a measurement vector based on the set of compressive measurements; and

a server for processing the source signal, the server configured to,

receive the measurement vector;

generate at least one first feature vector from the measurement vector, the first feature vector being an estimate of a second feature vector, the second feature vector corresponding to a translation of the source signal, the generating including generating more than one first feature vectors, each of the first feature vectors having a corresponding translation parameter;

compare a group of elements of a set of the first feature vectors with other elements of the set of first feature vectors;

determine which elements in the group of the set of the first feature vectors are similar to the other elements of the set of first feature vectors; and

detect an anomaly in the source signal based on the first feature vector.