![]() |
|
||
Sparselet Models for Efficient Multiclass Object DetectionHyun Oh Song, Stefan Zickler2, Tim Althoff, Ross Girshick3, Mario Fritz4, Christopher Geyer2, Pedro Felzenszwalb5, and Trevor Darrell 1UC Berkeley, USA
2iRobot, USA
3University of Chicago, USA
4Max Planck Institute for Informatics, Germany
5Brown University, USA
Abstract. 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. Keywords: Sparse Coding, Object Detection, Deformable Part Models LNCS 7573, p. 802 ff. lncs@springer.com
|