7859

Sparselet Models for Efficient Multiclass Object Detection

Hyun Oh Song, Stefan Zickler, Tim Althoff, Ross Girshick, Mario Fritz, Christopher Geyer, Pedro Felzenszwalb, Trevor Darrell
UC Berkeley
Proceedings of ECCV, 2012

@article{song2012sparselet,

   title={Sparselet Models for Efficient Multiclass Object Detection},

   author={Song, H.O. and Zickler, S. and Althoff, T. and Girshick, R. and Fritz, M. and Geyer, C. and Felzenszwalb, P. and Darrell, T.},

   year={2012}

}

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We develop intermediate representations for deformable part models, and show that such representations have favorable performance characteristics for multi-class problems where the number of classes is large. Our model uses sparse coding of part filters to represent each filter as a sparse linear combination of shared dictionary elements. This leads to an universal set of parts that are shared among all object classes. Reconstruction of part responses via sparse-matrix-multiply reduces computation relative to conventional filter convolutions. Our model is well suited to a parallel implementation, and we report a new GPU DPM implementation which 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.
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