1. Calculate HOC on Depth and HOG on RGB and concatenate them
Human Detection in RGBD using Histogram of Gradients and Curvature Shayan Modiri Assari† , Zach Robertson† †University of Central Florida
1. Problem
Human Detection:
Using depth information from devices such as the Microsoft Kinect as well as RGB data to improve human detection
Challenges:
Misalignment of RGB and Depth data
Depth data is very noisy and not very detailed
How to combine RGB and depth to receive good results
3. Our Method - Training
Histogram of Oriented Curvature
Gives a representation of the shape at each pixel
Mean and Gaussian Curvature
Smoothing should be applied on the image before calculation
Formulas
Mask 1
Mask 2
Mask 3
Mask n
Apply Masks
Aligned RGB Image
Aligned Depth Image
Align
Depth Image
RGB Image
Segment Depth Image
2. Previous Methods
Pedro Felzenszwalb’s Part- Based Method
Histogram of Oriented Gradients
Disadvantages
They do not take advantage of depth data
Each Color represents one of the shapes to the left
Get Masks from Depth
Calculate HOC on Depth and HOG on RGB and concatenate them
Learning
3.1 Alignment
The RGB and Depth data generated by the Microsoft Kinect are misaligned so realignment is needed
Multi-view geometry is used to realign them
4. Results
Results were promising but more testing will be done on harder datasets to finalize results …
HOC
3. Our Method - Testing
Segmentation
Done in order to remove background noise
Process
Segment aligned depth image with mean shift
Objects will be separated from background
Create a mask using segmented data
Apply mask on RGB and Original depth image
Result: Background noise removed
Sliding Window
Test against Model
True
False
Aligned RGB Image
Aligned Depth Image
Align
Depth Image
RGB Image
HOC +Hog
HOC +Hog