Location-aware event detection

1. A method for detecting one or more events, comprising the steps of:

using multiple overlapping regions of interest on a video sequence to cover a location for one or more events, wherein each event is associated with at least one of the multiple overlapping regions of interest;

applying multiple-instance learning to the video sequence to select one or more of the multiple overlapping regions of interest to construct one or more location-aware event models;

using a support vector machine- (SVM)-based multiple-instance learning technique to learn one or more event models for a pickup and a drop; and

applying the models to the video sequence to detect the one or more events and to determine the one or more regions of interest that are associated with the one or more events.

2. The method of claim 1 , wherein the one or more events comprise one or more events at a point of sale.

3. The method of claim 2 , wherein the one or more events at a point of sale comprise a pickup, a scan and a drop, wherein a pickup comprises a cashier picking up an item, a scan comprises a cashier at least one of reading the barcode on an item via a scanner and weighing an item, and a drop comprises a cashier placing an item onto a take-away belt area.

4. The method of claim 1 , wherein the multiple overlapping regions of interest comprise one or more shapes.

5. The method of claim 1 , wherein the multiple overlapping regions of interest comprise one or more sizes.

6. The method of claim 1 , further comprising extracting one or more features from each region of interest.

7. The method of claim 6 , wherein the one or more features comprise at least one of color, edge and motion.

8. A computer program product comprising a tangible non-transitory computer readable recordable storage medium having computer readable program code for detecting one or more events, said computer program product including:

computer readable program code for using multiple overlapping regions of interest on a video sequence to cover a location for one or more events, wherein each event is associated with at least one of the multiple overlapping regions of interest;

computer readable program code for applying multiple-instance learning to the video sequence to select one or more of the multiple overlapping regions of interest to construct one or more location-aware event models;

computer readable program code for using a support vector machine- (SVM)-based multiple-instance learning technique to learn one or more event models for a pickup and a drop; and

computer readable program code for applying the models to the video sequence to detect the one or more events and to determine the one or more regions of interest that are associated with the one or more events.

9. The computer program product of claim 8 , wherein the one or more events comprise one or more events at a point of sale.

10. The computer program product of claim 9 , wherein the one or more events at a point of sale comprise a pickup, a scan and a drop, wherein a pickup comprises a cashier picking up an item, a scan comprises a cashier at least one of reading the barcode on an item via a scanner and weighing an item, and a drop comprises a cashier placing an item onto a take-away belt area.

11. The computer program product of claim 8 , further comprising computer readable program code for extracting one or more features from each region of interest.

12. The computer program product of claim 11 , wherein the one or more features comprise at least one of color, edge and motion.

13. The computer program product of claim 8 , wherein the multiple overlapping regions of interest comprise one or more shapes and one or more sizes.

14. A system for detecting one or more events, comprising:

a memory; and

at least one processor coupled to said memory and operative to:

use multiple overlapping regions of interest on a video sequence to cover a location for one or more events, wherein each event is associated with at least one of the multiple overlapping regions of interest;

apply multiple-instance learning to the video sequence to select one or more of the multiple overlapping regions of interest to construct one or more location-aware event models;

use a support vector machine-(SVM)-based multiple-instance learning technique to learn one or more event models for a pickup and a drop; and

apply the models to the video sequence to detect the one or more events and to determine the one or more regions of interest that are associated with the one or more events.

15. The system of claim 14 , wherein the one or more events comprise one or more events at a point of sale.

16. The system of claim 15 , wherein the one or more events at a point of sale comprise a pickup, a scan and a drop, wherein a pickup comprises a cashier picking up an item, a scan comprises a cashier at least one of reading the barcode on an item via a scanner and weighing an item, and a drop comprises a cashier placing an item onto a take-away belt area.

17. The system of claim 14 , wherein the at least one processor coupled to said memory is further operative to extract one or more features from each region of interest.

18. The system of claim 17 , wherein the one or more features comprise at least one of color, edge and motion.

19. An apparatus for detecting one or more events, said apparatus comprising:

means for using multiple overlapping regions of interest on a video sequence to cover a location for one or more events, wherein each event is associated with at least one of the multiple overlapping regions of interest;

means for applying multiple-instance learning to the video sequence to select one or more of the multiple overlapping regions of interest to construct one or more location-aware event models;

means for using a support vector machine-(SVM)-based multiple-instance learning technique to learn one or more event models for a pickup and a drop; and

means for applying the models to the video sequence to detect the one or more events and to determine the one or more regions of interest that are associated with the one or more events.