Sparselet Models for Efficient Multiclass Object Detection
Title | Sparselet Models for Efficient Multiclass Object Detection |
Publication Type | Conference Paper |
Year of Publication | 2012 |
Authors | Song, H. Oh, Zickler S., Althoff T., Girshick R., Geyer C., Fritz M., Felzenszwalb P., & Darrell T. |
Page(s) | 802-815 |
Other Numbers | 3360 |
Keywords | Deformable Part Models, Object Detection, Sparse Coding |
Abstract | We develop an intermediate representation for deformablepart models and show that this representation has favorable performance characteristics formulti-class problems when the number of classes is high. Our model uses sparse coding of partfilters to represent each filter as a sparse linear combination of shared dictionary elements. Thisleads to a universal set of parts that are shared among all object classes. Re- construction of theoriginal part filter responses via sparse matrix-vector product reduces computation relative toconventional part filter convolutions. Our model is well suited to a parallel implementation, andwe report a new GPU DPM implementation that takes advantage of sparse coding of part filters. Thespeed-up offered by our intermediate representation and parallel computation enable real-time DPMdetection of 20 different object classes on a laptop computer. |
Acknowledgment | S. Zickler and C. Geyer were supported by DARPA con-tract W911NF-10-C-0081. P. Felzenszwalb and R. Girshick were supported inpart by NSF grant IIS-0746569. T. Darrell was supported by DARPA contractW911NF-10-2-0059, by NSF awards IIS-0905647, IIS-0819984, and support fromToyota and Google. |
URL | https://www.icsi.berkeley.edu/pubs/vision/sparseletmodels12.pdf |
Bibliographic Notes | Proceedings of the 12th European Conference on Computer Vision (ECCV 2012), pp. 802-815, Firenze, Italy |
Abbreviated Authors | H. O. Song, S. Zickler, T. Althoff, R. Girshick, C. Geyer, M. Fritz, P. Felzenszwalb, and T. Darrell |
ICSI Research Group | Vision |
ICSI Publication Type | Article in conference proceedings |