Height Estimation from Egocentric Video- Week 1 Dr. Ali Borji Aisha Urooj Khan Jessie Finocchiaro UCF CRCV REU 2016

Meetings  Monday- Met with Aisha and Dr. Borji  Went over the goals of the project  Aisha created an initial draft of the project goals and steps  Tuesday- Met with Aisha  Created a timeline of the project  Updated project report  Created Excel sheet with related papers  Wednesday  Implementing an SVM on images  Implementing k-means clustering on image descriptors  Thursday- Started data collection with Aisha  Continued SVM code

Stages of the project  1. Data Collection  2. Estimating if the wearer is short, average, or tall  3. Estimating height as a continuous measurement  4. Improving our accuracy

1. Data collection  Create our own dataset  10 people  Place the camera at 3 locations  Head  Chest  Waist  Simulates having 30 people: 10 of each category

Head camera

Chest camera

Waist camera

2. Estimating if the wearer is short, average, or tall (Coarse-level)  3 categories of height  Tall  Average  Short  Compute Gist features  Train SVM

3. Estimating height as a continuous measurement (Fine level)  Record the height (in cm) of the camera at each recording  Switch from SVM to CNN  Want a continuous measurement as the output  Not a category

4. Improving our accuracy  Continue training CNN  Add background movement  Goal: Estimation with +/- 5 cm of actual height.

Timeline

SVM so far

Keypoint generation

Summary  Video Processing  Reads in video and saves every nth frame  Generate data  Video is data for phase 2  SVM  Reads in all files in a folder, flattens them, and returns a 2D array with each image represented as a row  Like MNIST  Initializes, trains, and saves a SVM for the images  Image Descriptors  Wrote an example script using k-means clustering of the keypoints generated  Implemented ORB (Oriented BRIEF) to generate local keypoints and extract image descriptors  Used pyleargist to extract global image descriptors from images