We develop an intermediate representation for deformable part models
and show that this representation has favorable performance
characteristics for multi-class problems when the number of classes is
high. Our model uses sparse coding of part filters to represent
each filter as a sparse linear combination of shared dictionary
elements. This leads to a universal set of
parts that are shared among all object classes.
Reconstruction of the original part filter responses via sparse matrix-vector product reduces
computation relative to conventional part filter convolutions. Our model is
well suited to a parallel implementation, and we report a new GPU DPM
implementation that takes advantage of sparse coding of part filters.
The speed-up offered by our intermediate representation and parallel
computation enable real-time DPM detection of 20 different
object classes on a laptop computer.
Hyun Oh Song, Stefan Zickler,Tim Althoff, Ross Girshick, Mario Fritz,
Christopher Geyer, Pedro Felzenszwalb, Trevor Darrell
Sparselet Models for Efficient Multiclass Object Detection
Proceedings of ECCV 2012.