Methods and apparatus, including computer program products, implementing and using techniques for configuring an artificial neural network to a particular surveillance situation. A number of object classes characteristic for the surveillance situation are selected. The object classes form a subset of the total number of object classes for which the artificial neural network is trained. A database is accessed that includes activation frequency values for the neurons within the artificial neural network. The activation frequency values are a function of the object class. Those neurons having activation frequency values lower than a threshold value for the subset of selected object classes are removed from the artificial neural network.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for configuring an artificial neural network to a particular surveillance situation, the method comprising: selecting a number of object classes characteristic for the surveillance situation, wherein the object classes form a subset of the total number of object classes for which the artificial neural network is trained; accessing a database including activation frequency values for the neurons within the artificial neural network, wherein the activation frequency values are a function of the object class; and removing from the artificial neural network those neurons having activation frequency values lower than a threshold value for the subset of selected object classes, wherein the removed number of neurons is such that a reduction in computational resources needed for calculating the output from the artificial neural network outweighs an increase in computational resources needed for maintaining records about the removed neurons. 2. The method of claim 1, wherein the threshold value is a static value determined for each class during a training/test procedure for the artificial neural network. 3. The method of claim 1, wherein the threshold value is adaptive for each class based on artificial neural network performance. 4. The method of claim 1, further comprising: refining the selection of object classes characteristic for the surveillance situation, based on data collected over a certain time period; and repeating the selecting, accessing and removing steps for the refined selection of object classes. 5. The method of claim 1, wherein at least some of the selecting, accessing, and removing steps are performed in response to a user input received through a user interface. 6. The method of claim 1, wherein, in a fully connected artificial neural network, removing neurons corresponds to removing rows and columns from matrices forming mathematical representations of individual layers of the artificial neural network, thereby reducing the number of computations needed in calculating the output from the artificial neural network. 7. The method of claim 1, wherein the artificial neural network is deployed in an embedded device. 8. A computer program for configuring an artificial neural network to a particular surveillance situation, the program contains instructions corresponding to the steps of: selecting a number of object classes characteristic for the surveillance situation, wherein the object classes form a subset of the total number of object classes for which the artificial neural network is trained; accessing a database including activation frequency values for the neurons within the artificial neural network, wherein the activation frequency values are a function of the object class; and removing from the artificial neural network those neurons having activation frequency values lower than a threshold value for the subset of selected object classes, wherein the removed number of neurons is such that a reduction in computational resources needed for calculating the output from the artificial neural network outweighs an increase in computational resources needed for maintaining records about the removed neurons. 9. The computer program of claim 8, wherein the threshold value is a static value determined for each class during a training/test procedure for the artificial neural network. 10. The computer program of claim 8, wherein the threshold value is adaptive for each class based on artificial neural network performance. 11. The computer program of claim 8, further comprising program instructions corresponding to the steps of: refining the selection of object classes characteristic for the surveillance situation, based on data collected over a certain time period; and repeating the selecting, accessing and removing steps for the refined selection of object classes. 12. The computer program of claim 8, wherein, in a fully connected artificial neural network, removing neurons corresponds to removing rows and columns from matrices forming mathematical representations of individual layers of the artificial neural network, thereby reducing the number of computations needed in calculating the output from the artificial neural network. 13. A digital storage medium comprising a computer program as claimed in claim 8.