Demo Code Review Travis Desell Department of Computer Science University of North Dakota February 19, 2014 Grand Forks, ND Susan Ellis-Felege Department of Biology University of North Dakota

Code Review Requirements

You should have at least the following 5 sections in your presentation: 1.Problem definition 2.Demo 3.Software Overview Diagram and Explanation 4.Code Presentation & Database Schema (if applicable) 5.Progress & Future Work Also: 1.Code must be in GitHub 2.Code must be well commented 3.At least 3 TODOs in comments per team member 4.Specify what code/files have been written by what team member

1.Problem definition. 2.Demo. 3.Software Overview Diagram and Explanation 4.Code Presentation 5.Progress & Future Work Overview

Problem Definition

During the summers of 2012 and 2013, Dr. Ellis-Felege gathered 50,000 hours of avian nesting video from the following species: 1.Sharp-tailed grouse (Tympanuchus phasianellus), an important game bird and wildlife health indicator species (~35.5k hours). 2.Piping plovers (Charadrius melodus), a federally listed threatened species (~3.3k hours). 3.Interior least terns (Sternula antillarum), a federally listed endangered species (~10.6k hours). At least 60,000 more hours are expected by the end of the project.

All three species are ground nesting birds. Sharp-tailed grouse nest in the dense grass (top left). Nests were monitored in areas of high oil development, moderate oil development and no oil development (protected state land). Piping plover and interior least tern are shore nesting species (top right). Nests were monitored along the Missouri River in North Dakota. Sharp-tailed Grouse Piping Plover

What’s the point? 1.Current cameras that use automated motion detection miss some predators and are not robust enough). 2.Camera footage allows Dr. Ellis-Felege to manage and evaluate studies with large enough sample sizes for statistical significance. 3.Answer biological questions about parental investment and predator-prey interactions for these ground nesting species. 4.Examine the effect of oil development on wildlife in western North Dakota, which is experiencing a boom in fracking.

Demo: Crowd Sourcing Interface

Volunteers enter observations via a a webpage which streams video, marking yes/no/unsure for each type of observation. A validator awards users credit and accuracy when a quorum is reached for each observation type.

Software Diagram and Explanation

1. Video is brought back from the field (where there is no or limited Internet access) and uploaded to the servers.

2. A daemon discovers newly uploaded videos and adds them to the video database.

3. Another daemon converts the video into formats for web streaming (generating 3, 5, 10, and 20 minute segments) and for analysis by volunteered computers.

4. Webpages determine what videos to display to users and allow them to record their observations.

5. BOINC is used to send workunits out and collect results of the computer vision and motion detection techniques.

6. A scientific web portal allows project scientists to enter their own video observations and compare crowd sourced results and automated techniques.

Code Presentation

Database Schema

code overview watch.php is what displays the video and gives the radio buttons for users to select yes/no/unsure for various categories. watch.js is used to handle all the elements in watch.php, and it will submit a users observations with the report_observations.php script. This returns some JSON with results from the database that allow a modal to be populated with the observations from other users. Finally, the crowd_observation_validator.cxx reads user observations from the database to determine if they are correctly validated against other observations and awards users credit.

watch.php ebpage/watch.php

watch.js ebpage/watch.js

report_observation.php ebpage/report_observation.php

crowd_observation_validator.c xx oinc_daemons/crowd_observation_validator.cxx

Progress and Future Work

Recent Progress and Future Work The project now has had almost 200 users watch video, and many are highly active. Now over 7000 hours of video watched and validated from users, and there are over 300,000 observed videos. Future work includes comparing user accuracy using various viewing segment lengths, and developing new interfaces to allow more users to provide more detailed descriptions of what’s happening in the video.