Stationary target detection by exploiting changes in background model
A sequence of video frames of an area of interest is obtained. A first background model of the area of interest is constructed based on a first parameter. A second background model of the area of interest is constructed based on a second parameter, the second parameter being different from the first parameter. A difference between the first and second background models is determined. A stationary target is determined based on the determined difference. An alert concerning the stationary target is generated.
1. A computer-implemented method for processing one or more video frames, the method comprising:
generating, using the one or more video frames, a first background model with an associated first learning rate;
generating, using the one or more video frames, a second background model with an associated second learning rate different than the first learning rate;
comparing the first background model and the second background model to generate a comparison; and
generating one or more blobs in response to the comparison.
2. The computer-implemented method as claimed in claim 1 further comprising classifying each of the one or more blobs using an artificial neural network classifier.
3. The computer-implemented method as claimed in claim 2 wherein the artificial neural network classifier is configured to classify the one or more blobs in at least a first class of object and a second class of object different than the first class.
4. The computer-implemented method as claimed in claim 3 wherein the first class of object is a person.
5. The computer-implemented method as claimed in claim 4 wherein the first class of object is a vehicle.
6. The computer-implemented method as claimed in claim 4 wherein the first class of object is a piece of luggage.
7. The computer-implemented method as claimed in claim 1 wherein the second learning rate is slower than the first learning rate.
8. The computer-implemented method as claimed in claim 1 further comprising performing salience filtering to filter out erroneous results.
9. The computer-implemented method as claimed in claim 1 further comprising carrying out classifications of the one or more blobs by performing a plurality of gradients calculations taking into account the first and second background models.
10. A non-transitory computer-readable medium containing instructions which, when executed on a processor, perform a method for processing one or more video frames, the method comprising:
generating, using the one or more video frames, a first background model with an associated first learning rate;
generating, using the one or more video frames, a second background model with an associated second learning rate different than the first learning rate;
comparing the first background model and the second background model to generate a comparison; and
generating one or more blobs in response to the comparison.
11. The non-transitory computer-readable medium as claimed in claim 10 wherein the method further includes classifying each of the one or more blobs using an artificial neural network classifier.
12. The non-transitory computer-readable medium as claimed in claim 11 wherein the artificial neural network classifier is configured to classify the one or more blobs in at least a first class of object and a second class of object different than the first class.
13. The non-transitory computer-readable medium as claimed in claim 12 wherein the first class of object is a person.
14. The non-transitory computer-readable medium as claimed in claim 12 wherein the first class of object is a vehicle.
15. The non-transitory computer-readable medium as claimed in claim 12 wherein the first class of object is a piece of luggage.
16. The non-transitory computer-readable medium as claimed in claim 10 wherein the second learning rate is slower than the first learning rate.
17. The non-transitory computer-readable medium as claimed in claim 10 wherein the method further includes performing salience filtering to filter out erroneous results.
18. The non-transitory computer-readable medium as claimed in claim 10 wherein the method further includes carrying out classifications of the one or more blobs by performing a plurality of gradients calculations taking into account the first and second background models.