کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
8845782 1617187 2018 45 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Classification of bird species from video using appearance and motion features
ترجمه فارسی عنوان
طبقه بندی گونه های پرنده از ویدیو با استفاده از ویژگی های ظاهر و حرکت
کلمات کلیدی
ویژگی های ظاهر، ویژگی های حرکتی، استخراج ویژگی، انتخاب ویژگی، طبقه بندی پرندگان، طبقه بندی دقیق دانه
موضوعات مرتبط
علوم زیستی و بیوفناوری علوم کشاورزی و بیولوژیک بوم شناسی، تکامل، رفتار و سامانه شناسی
چکیده انگلیسی
A number of existing algorithms are able to classify bird species from individual high quality detailed images often using manual inputs (such as a priori parts labelling). However, deployment in the field necessitates fully automated in-flight classification, which remains an open challenge due to poor image quality, high and rapid variation in pose, and similar appearance of some species. We address this as a fine-grained classification problem, and have collected a video dataset of thirteen bird classes (ten species and another with three colour variants) for training and evaluation. We present our proposed algorithm, which selects effective features from a large pool of appearance and motion features. We compare our method to others which use appearance features only, including image classification using state-of-the-art Deep Convolutional Neural Networks (CNNs). Using our algorithm we achieved an 90% correct classification rate, and we also show that using effectively selected motion and appearance features together can produce results which outperform state-of-the-art single image classifiers. We also show that the most significant motion features improve correct classification rates by 7% compared to using appearance features alone.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Ecological Informatics - Volume 48, November 2018, Pages 12-23
نویسندگان
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