3. Our Method - Training

Human Detection in RGBD using Histogram of Gradients and Curvature Shayan Modiri Assari† (smodiri@eecs.ucf.edu) , Zach Robertson† (zachrobertson@knights.ucf.edu)

†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

3.1 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

3.3. 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

2. Previous Methods Pedro Felzenszwalb’s Part- Based Method Histogram of Oriented Gradients

Disadvantages They do not take advantage of depth data

4. Results Results were promising but more testing will be done on harder datasets to

finalize results

3.2. 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

Each Color represents one of the shapes to the left

HOC +Hog

HOC

HOC +Hog

…Mask 1 Mask 2 Mask 3 Mask n

Apply Masks

Aligned RGB Image

Aligned Depth Image

Align

Depth ImageRGB Image

Segment Depth Image

Get Masks from

Depth

Calculate HOC on Depth and HOG on RGB and concatenate

them

Learning

Sliding Window

Test against Model

True

False

Aligned RGB Image

Aligned Depth Image

Align

Depth ImageRGB Image

3. Our Method - Testing