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

  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},
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
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