Calculate HOC on Depth and HOG on RGB and concatenate them 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. Our Method - Training 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 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 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 Sliding Window Test against Model True False Aligned RGB Image Aligned Depth Image Align Depth Image RGB Image HOC +Hog HOC +Hog