Object recognition for an intelligent room
Richard J. Campbell, John C. Krumm · 2002
Intelligent rooms equipped with video cameras can exhibit compelling behaviors, many of which depend on object recognition. Unfortunately, object recognition algorithms are rarely written with a normal consumer in mind, leading to programs that would be impractical to use for a typical person. These impracticalities include speed of execution, elaborate training rituals, and setting adjustable parameters. We present an algorithm that can be trained with only a few images of the object, that requires only two parameters to be set, and that runs at 0.7 Hz on a normal PC with a normal color camera. The algorithm represents an object’s features as small, quantized edge templates, and it represents the object’s geometry with “Hough kernels”.