Automatic nesting seabird detection based on boosted HOG-LBP descriptors

Seabird populations are considered an important and accessible indicator of the health of marine environments: variations have been linked with climate change and pollution 1. However, manual monitoring of large populations is labour-intensive, and requires significant investment of time and effort. In this paper, we propose a novel detection system for monitoring a specific population of Common Guillemots on Skomer Island, West Wales (UK). We incorporate two types of features, Histograms of Oriented Gradients (HOG) and Local Binary Pattern (LBP), to capture the edge/local shape information and the texture information of nesting seabirds. Optimal features are selected from a large HOG-LBP feature pool by boosting techniques, to calculate a compact representation suitable for the SVM classifier. A comparative study of two kinds of detectors, i.e., whole-body detector, head-beak detector, and their fusion is presented. When the proposed method is applied to the seabird detection, consistent and promising results are achieved. © 2011 IEEE

Estimating seabird abundance: a case study in Kenai Fjords National Park, Alaska

Thesis (M.S.) University of Alaska Fairbanks, 2018Estimation of breeding seabird population size and trends are integral components of studies or programs seeking to understand how seabird populations respond to changes in marine or coastal environments, to identify threatened or declining species, and to inform management actions and decisions. In Chapter 1, I conduct a review of the challenges, considerations, tools, and methods involved in efforts to estimate and monitor breeding seabird abundance. I discuss challenges in terms of two broad categories: 1) seabird life history, behavior, and breeding environments, and 2) challenges inherent to survey methods and logistics. I introduce methods and tools used to access seabird colonies, detect birds, and design methods to collect and analyze count or abundance data. The focus of Chapter 2 is to find effective methods to estimate the breeding abundance of glaucous-winged gulls (Larus glaucescens) in Kenai Fjords National Park (KEFJ), Alaska, which has been designated as an Important Bird Area (IBA) for this species. There are numerous inherent challenges in this effort, as L. glaucescens breeds in widespread colonies on vertical cliff faces of the fjords and associated islands, and their nests are not visually detectable from boat-based surveys. I conducted and compared field counts to replicated photographic counts, and found enough variability between replicates for both count methods to preclude calculation of precise abundance estimates using counts alone. I then developed a more intensive method of analyzing images using a modified mark-resight (MR) approach to identify all potential nest locations, and I took advantage of both attendance and behavioral data collected from repeat photographs to estimate what proportion of them have a high probability of containing nests. I quantified two potential survey error rates and their effects on the results of our modified MR approach. Finally, I considered temporal and environmental factors likely to affect both repeated counts and the results of my modified MR approach. I found that: 1) the modified MR approach provided a better approximation of breeding abundance than simple field counts and addressed variability between replicate surveys; 2) low misidentification survey error rates had a negligible effect on the results; and 3) general patterns of attendance of birds at colonies were influenced by different factors than the attendance patterns at locations that were likely nests. I recommend similar methods for other colonial or cliff-nesting bird species, species that have variable attendance, or species that make nests that are hard to see. These methods may also be helpful in areas that are remote or infrequently visited or where time in the field is a limiting factor in how much data can be collected.Chapter 1. Challenges, methods, tools and considerations for monitoring seabird abundance -- Chapter 2. Estimating breeding population size of a cliff-nesting bird species -- Conclusion -- Appendices

Deep learning for animal recognition

Deep learning has obtained many successes in different computer vision tasks such as classification, detection, and segmentation of objects or faces. Many of these successes can be ascribed to training deep convolutional neural network architectures on a dataset containing many images. Limited research has explored deep learning methods for performing recognition or detection of animals using a limited number of images. This thesis examines the use of different deep learning techniques and conventional computer vision methods for performing animal recognition or detection with relatively small training datasets and has the following objectives: 1) Analyse the performance of deep learning systems compared to classical approaches when there exists a limited number of images of animals; 2) Develop an algorithm for effectively dealing with rotation variation naturally present in aerial images; 3) Construct a computer vision system that is more robust to illumination variation; 4) Analyse how important the use of different color spaces is in deep learning; 5) Compare different deep convolutional neural-network algorithms for detecting and recognizing individual instances (identities) in a group of animals, for example, badgers. For most of the experiments, effectively reduced neural network recognition systems are used, which are derived from existing architectures. These reduced systems are compared to standard architectures and classical computer vision methods. We also propose a color transformation algorithm, a novel rotation-matrix data-augmentation algorithm and a hybrid variant of such a method, that factors color constancy with the aim to enhance images and construct a system that is more robust to different kinds of visual appearances. The results show that our proposed algorithms aid deep learning systems to become more accurate in classifying animals for a large number of different animal datasets. Furthermore, the developed systems yield performances that significantly surpass classical computer vision techniques, even with limited amounts of available images for training

Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE