  Object recognition and reconstruction is of great interest in many research
fields. Detecting pedestrians or cars in traffic cameras or tracking seismic
waves in geophysical exploration are only two of many applications. Recently,
the authors developed a new method - Object reconstruction using
K-approximation (ORKA) - to extract such objects out of given data. In this
method a special object model is used where the movement and deformation of the
object can be controlled to fit the application. ORKA in its current form is
highly dependent on the data resolution. On the one hand, the movement of the
object can only be reconstructed on a grid that depends on the data resolution.
On the other hand, the runtime increases exponentially with the resolution.
Hence, the resolution of the data needs to be in a small range where the
reconstruction is accurate enough but the runtime is not too high. In this
work, we present a multiresolution approach, where we combine ORKA with a
wavelet decomposition of the data. The object is then reconstructed iteratively
what drastically reduces the runtime. Moreover, we can increase the data
resolution such that the movement reconstruction no longer depends on the
original grid. We also give a brief introduction on the original ORKA
algorithm. Hence, knowledge of the previous work is not required.
