Volpe and Koenig, P.C.
Artificial neural network class-based pruning
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.
Axis AB
G G G
G06N G06K G06K
inventional inventional inventional
Hanna Martin Jiandan Robin
Björgvinsdóttir Ljungqvist Chen Seibold