Automatic nesting seabird detection based on boosted HOG-LBP descriptors

Qing, Chunmei and Dickinson, Patrick and Lawson, Shaun and Freeman, Robin (2011) Automatic nesting seabird detection based on boosted HOG-LBP descriptors. In: Conference of 2011 18th IEEE International Conference on Image Processing, ICIP 2011, 11-14 September 2011, Brussels, Belgium.

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Abstract

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.

Additional Information:Conference Code: 88213
Keywords:Automatic nesting, Compact representation, Comparative studies, Descriptors, Detection system, HOG, Labour-intensive, Large population, LBP, Local binary patterns, Manual monitoring, Marine environment, Shape information, SVM, SVM classifiers, Texture information, Whole-body, Adaptive boosting, Climate change, Content based retrieval, Image processing, Marine pollution, Pollution detection, Detectors
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:8683
Deposited On:09 Apr 2013 10:07

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