HON4D: Histogram of Oriented 4D Normals for Activity Recognition from Depth Sequences

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 u...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:2013 IEEE Conference on Computer Vision and Pattern Recognition S. 716 - 723
Hauptverfasser: Oreifej, Omar, Zicheng Liu
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.06.2013
Schlagworte:
4D Normals
Action Recognition
Activity Recognition
Depth
Histogram of Gradients
Histogram of Normals
Histograms
HOG
HON
Joints
Kinect
MSR Action 3D
MSR Action Pairs
MSR Daily Activity
Polychoron
Quantization (signal)
Shape
Support vector machines
Three-dimensional displays
Vectors
ISSN:1063-6919, 1063-6919
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2013.98