Using Computer Vision And Volunteer Computing To Analyze Avian Nesting Patterns And Reduce Scientist Workload

This paper examines the use of feature detection and background subtraction algorithms to classify and detect events of interest within uncontrolled outdoor avian nesting video from the Wildlife@Home project. We tested feature detection using Speeded Up Robust Features (SURF) and a Support Vector Machine (SVM) along with four background subtraction algorithms — Mixture of Guassians (MOG), Running Gaussian Average (AccAvg), ViBe, and Pixel-Based Adaptive Segmentation (PBAS) — as methods to automatically detect and classify events from surveillance cameras. AccAvg and modified PBAS are shown to provide robust results and compensate for issues caused by cryptic coloration of the monitored species. Both methods utilize the Berkeley Open Infrastructure for Network Computing (BOINC) in order to provide the resources to be able to analyze the 68,000+ hours of video in the Wildlife@Home project in a reasonable amount of time. The feature detection technique failed to handle the many challenges found in the low quality uncontrolled outdoor video. The background subtraction work with AccAvg and the modified version of PBAS is shown to provide more accurate detection of events

Using Citizen Scientists To Inform Machine Learning Algorithms To Automate The Detection Of Species In Ecological Imagery

Modern data collection techniques used by ecologists has created a deluge of data that is becoming increasingly difficult to store, filter, and analyze in an efficient and timely manner. In just two summers, over 65,000 unmanned aerial system (UAS) images were collected, comprising the several terabytes (TB) of data that was reviewed by citizen scientists to generate inputs for machine learning algorithms. Uncontrolled conditions and the small size of target species relative to the background further increase the difficulty of manually cataloging the images. To assist with locating and identifying snow geese in the UAS images, a citizen science web portal was created as part of Wildlife@Home. It is demonstrated that aggregate citizen scientist observations are similar in quality to observations made by trained experts and can be used to train convolutional neural networks (CNN) to automate the detection of species in the imagery. Using a dataset comprising of the aggregate observations produces consistently better results than datasets consisting of observations from a single altitude, indicating that more numerous but slightly variable observations is preferable to more consistent but less numerous observations. The framework developed requires system administrators to manually run scripts to populate the database with new images; however, this framework can be extended to allow researchers to create their own projects, upload new images, and download data for CNN training

Training Convolutional Neural Networks Using An Automated Feedback Loop To Estimate The Population Of Avian Species

Using automated processes to detect wildlife in uncontrolled outdoor imagery in the field of wildlife ecology is challenging task. This is especially true in imagery provided by an Unmanned Aerial System (UAS), where the relative size of wildlife is small and visually similar to its background. In the UAS imagery collected by the Wildlife@Home project, the data is also extremely unbalanced, with less than 1% of area in the imagery being of wildlife. To tackle these challenges, the Wildlife@Home project has employed citizen scientists and trained experts to go through collected UAS imagery and classify it. Classified data are used as inputs to convolutional neural networks (CNNs) which seek to automatically mark which areas of the imagery contain wildlife. The output of the CNN is then passed to a blob counter which returns a population estimate for the image. A feedback loop was developed to help train the CNNs to better differentiate between the wildlife and the the visually similar background and deal with the disparate amount of wildlife training images versus background training images. When using the feedback loop and citizen scientist provided data, population estimates by the CNN and blob counter are within 3.93% of the manual count by the field biologists. When expert provided data is used the estimates are within 5.24%. This is improved from 150% and 88% error in previous work which did not employ a feedback loop for the citizen science and expert data, respectively. Citizen scientist data worked better than expert data in the current work potentially because a matching algorithm was used on the citizen scientist data but not the expert data

2020 Student Symposium Research and Creative Activity Book of Abstracts

The UMaine Student Symposium (UMSS) is an annual event that celebrates undergraduate and graduate student research and creative work. Students from a variety of disciplines present their achievements with video presentations. It’s the ideal occasion for the community to see how UMaine students’ work impacts locally – and beyond. The 2020 Student Symposium Research and Creative Activity Book of Abstracts includes a complete list of student presenters as well as abstracts related to their works

Measuring Behavior 2018 Conference Proceedings

These proceedings contain the papers presented at Measuring Behavior 2018, the 11th International Conference on Methods and Techniques in Behavioral Research. The conference was organised by Manchester Metropolitan University, in collaboration with Noldus Information Technology. The conference was held during June 5th – 8th, 2018 in Manchester, UK. Building on the format that has emerged from previous meetings, we hosted a fascinating program about a wide variety of methodological aspects of the behavioral sciences. We had scientific presentations scheduled into seven general oral sessions and fifteen symposia, which covered a topical spread from rodent to human behavior. We had fourteen demonstrations, in which academics and companies demonstrated their latest prototypes. The scientific program also contained three workshops, one tutorial and a number of scientific discussion sessions. We also had scientific tours of our facilities at Manchester Metropolitan Univeristy, and the nearby British Cycling Velodrome. We hope this proceedings caters for many of your interests and we look forward to seeing and hearing more of your contributions