The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
HON4D: Histogram of Oriented 4D Normals for Activity Recognition from Depth Sequences
Open accessAbstract
We present a new descriptor for activity recognition from videos acquired by a depth sensor. Previous descriptors mostly compute shape and motion features independently, thus, they often fail to capture the complex joint shape-motion cues at pixel-level. In contrast, we describe the depth sequence using a histogram capturing the distribution of the surface normal orientation in the 4D space of time, depth, and spatial coordinates. To build the histogram, we create 4D projectors, which quantize the 4D space and represent the possible directions for the 4D normal. We initialize the projectors using the vertices of a regular polychoron. Consequently, we refine the projectors using a discriminative density measure, such that additional projectors are induced in the directions where the 4D normals are more dense and discriminative. Through extensive experiments, we demonstrate that our descriptor better captures the joint shape-motion cues in the depth sequence, and thus outperforms the state-of-the-art on all relevant benchmarks.
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
Cited by
NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
Shahroudy, A., Liu, J., Ng, T.-T., & Wang, G. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.115 · Cited by 3,148
A Low Power, Fully Event-Based Gesture Recognition System
Amir, A., Taba, B., Berg, D., Melano, T., McKinstry, J., Di Nolfo, C., Nayak, T., Andreopoulos, A., Garreau, G., Mendoza, M., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.781 · Cited by 1,017
A Comprehensive Survey of Vision-Based Human Action Recognition Methods
Zhang, H.-B., Zhang, Y.-X., Zhong, B., Lei, Q., Yang, L., Du, J.-X., & Chen, D.-S. (2019) · Sensors · doi:10.3390/s19051005 · Cited by 503
“[16] extended the HOG feature to the spatiotemporal depth structure, and proposed an orientation histogram feature of the four-dimensional normal vector to represent the appearance information of a three-dimensional spatiotemporal depth structure.”
BackgroundA survey of depth and inertial sensor fusion for human action recognition
Chen, C., Jafari, R., & Kehtarnavaz, N. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-015-3177-1 · Cited by 356
Recognizing Human Actions as the Evolution of Pose Estimation Maps
Liu, M., & Yuan, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00127 · Cited by 336
Metadata
- Authors
- Oreifej, O., & Liu, Z.
- Venue
- 2013 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2013-06
- Pages
- 716-723
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2085735683
- Cited by
- 956
- References
- 3
- FWCI
- 72.11
- Type
- conference paper
- Language
- en
- Open access
- green
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