Spatio-temporal Depth Cuboid Similarity Feature for Activity Recognition Using Depth Camera

@article{Xia2013SpatiotemporalDC,
  title={Spatio-temporal Depth Cuboid Similarity Feature for Activity Recognition Using Depth Camera},
  author={Lu Xia and Jake K. Aggarwal},
  journal={2013 IEEE Conference on Computer Vision and Pattern Recognition},
  year={2013},
  pages={2834-2841}
}
Local spatio-temporal interest points (STIPs) and the resulting features from RGB videos have been proven successful at activity recognition that can handle cluttered backgrounds and partial occlusions. In this paper, we propose its counterpart in depth video and show its efficacy on activity recognition. We present a filtering method to extract STIPs from depth videos (called DSTIP) that effectively suppress the noisy measurements. Further, we build a novel depth cuboid similarity feature… CONTINUE READING
Highly Influential
This paper has highly influenced 58 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 446 citations. REVIEW CITATIONS

From This Paper

Figures, tables, results, connections, and topics extracted from this paper.
208 Extracted Citations
31 Extracted References
Similar Papers

Citing Papers

Publications influenced by this paper.
Showing 1-10 of 208 extracted citations

447 Citations

05010015020142015201620172018
Citations per Year
Semantic Scholar estimates that this publication has 447 citations based on the available data.

See our FAQ for additional information.

Referenced Papers

Publications referenced by this paper.
Showing 1-10 of 31 references

Similar Papers

Loading similar papers…