Sparselet Models for Efficient Multiclass Object Detection

(Real-Time Multiclass Object Recognition on a Laptop Computer)

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.

Demo Video

References

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.
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