Automatic classification of flying bird species using computer vision techniques

Atanbori, John and Duan, Wenting and Appiah, Kofi and Murray, John and Dickinson, Patrick (2016) Automatic classification of flying bird species using computer vision techniques. Pattern Recognition Letters, 81 . pp. 53-62. ISSN 0167-8655

Documents
Automatic classification of flying bird species using computer vision techniques
Marked In Press, Corrected Proof. Available online from 3.9.15
[img]
[Download]
18588 additional Automatic classification of flying bird species using computer vision techniques.pdf

Request a copy
[img]
Preview
PDF
__network.uni_staff_S2_jpartridge_1-s2.0-S0167865515002743-main.pdf - Whole Document

1MB
[img] PDF
18588 additional Automatic classification of flying bird species using computer vision techniques.pdf - Supplemental Material
Restricted to Repository staff only

36kB
Item Type:Article
Item Status:Live Archive

Abstract

Bird populations are identified as important biodiversity indicators, so collecting reliable population data is important to ecologists and scientists. However, existing manual monitoring methods are labour-intensive, time-consuming, and potentially error prone. The aim of our work is to develop a reliable automated system, capable of classifying the species of individual birds, during flight, using video data. This is challenging, but appropriate for use in the field, since there is often a requirement to identify in flight, rather than while stationary. We present our work, which uses a new and rich set of appearance features for classification from video. We also introduce motion features including curvature and wing beat frequency. Combined with Normal Bayes classifier and a Support Vector Machine classifier, we present experimental evaluations of our appearance and motion features across a data set comprising 7 species. Using our appearance feature set alone we achieved a classification rate of 92% and 89% (using Normal Bayes and SVM classifiers respectively) which significantly outperforms a recent comparable state-of-the-art system. Using motion features alone we achieved a lower-classification rate, but motivate our on-going work which we seeks to combine these appearance and motion feature to achieve even more robust classification.

Keywords:Fine-grained classification, Computer vision, Ecology, Bird Species, Motion features, Appearance Features, NotOAChecked
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:18588
Deposited On:18 Sep 2015 08:02

Repository Staff Only: item control page