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1 Challenge the future HON4D: Histogram of Oriented 4D Normals for Activity Recognition from Depth Sequences Omar Oreifej Zicheng Liu CVPR 2013

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2 Challenge the future Research Question Input: Depth sequences information (only) of segmented video (1 video for 1 activity) Output: Feature (activity descriptor) for Activity recognition: Classify the activity Why use depth information rather than color information? Depth reflects pure geometry and shape cues. Depth is insensitive to changes in lighting conditions.

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3 Challenge the future Two key points about activity recognition feature Capture the shape cues at a specific time instance Capture the motion cues over the time

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4 Challenge the future HON4D: Histogram of Oriented 4D Normals Analogous to HOG feature Calculated in 4D space: 2D images (x, y), depth (z), time (t) Surface normals capture the shape cues Change in the surface normals over time capture the motion cues. Normals in 3D example

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5 Challenge the future 4D surface normal Depth (z) considered as a function of time (t), space (x, y) A surface S in a 4D space The normal to the surface S is

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6 Challenge the future Histogram of 4D normals How to quantize the 4D space, i.e., get the bin of histogram? Polychoron: 4D regular geometric objects, analogous to cube in 3D space Dvide the 4D space uniformly with its vertices use 600-cell polychoron with 120 vertices Each vertex is referred as a projector, i.e., one bin of histogram. Histogram: Project 4D normals into 120 projectors HON4D: 120-dimension feature

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7 Challenge the future Histogram of 4D normals Projection of 4D normals set of 120 projectors set of unit normals computed over all depth sequences Projection with inner product 120-dimensional HON4D descriptor feature by Normalization

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8 Challenge the future Non-Uniform Quantization == Projectors Refinement Uniform space quantization is not always optimal. A better Non-Uniform Quantization could lead to better classification. = refine the projectors to better capture the distribution of the normals. How to evaluate the importance of each projector?

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9 Challenge the future Non-Uniform Quantization == Projectors Refinement First intuition: calculate the projector density is the training video set. High density does not necessarily means high contribution in classification

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10 Challenge the future Recall SVM

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11 Challenge the future Non-Uniform Quantization == Projectors Refinement Consider a SVM classifier for the training set, is the set of support vectors in the training set. Discriminative projector density 1.High accumulation of normal vectors 2.High contribution in the final classification

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12 Challenge the future Projectors Refinement Augment the density-learned projectors In experiments: 120 300

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13 Challenge the future Flow chart

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14 Challenge the future Experiments

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