Classification of bird species from video using appearance and motion features

Abstract

The monitoring of bird populations can provide important information on the state of sensitive ecosystems; however, the manual collection of reliable population data is labour-intensive, time-consuming, and potentially error prone. Automated monitoring using computer vision is therefore an attractive proposition, which could facilitate the collection of detailed data on a much larger scale than is currently possible.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-ofthe-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.

Birds Identification System using Deep Learning

“…Table X has approved that the output of our proposal can be considered as one of the interesting study comapred to the previous researchs, for several reasons: 1) Some of previous studies were conducted on small dataset birds (categories) like in [4], [7] that used (13), ( 16) categories recpectively, compared to this study that used (434).…”

“…Another study conducted by [4] in field of database birds based images and birds identification system. Their study aimed to classification the birds during flight from video clips.…”

“…Then deep learning algorithm will convert uploaded image into gray scale format and apply that image on train model to predict best match species name for the uploaded image. Also, during the previous years, artificial intelligence is used in the field of bird watching based images using different algorithms and methods [1][3] [4] [7] [14], but this study differs from others in using the following operations: combine between the fc6/fc7, max between fc6/fc7, min between fc6/fc7, and the average for fc6/fc7 based on VGG-19. Hence, the field of birdwatching needs more investigations to develop systems with new technique that help to identify birds.…”

Brazilian Birds of Prey - A New Dataset and Classification with Deep Neural Networks

“…In classification using images, [9] uses computer vision techniques that include segmentation and a feature set of appearance and motion. From the same author, [10] improves the results using the features allied with classifiers, being able to achieve 90% correct classification in their bird's flight dataset.…”

Experimental Evaluation of a Novel Sensor-Based Sorting Approach Featuring Predictive Real-Time Multiobject Tracking

“…The data from the tracking is here used to measure the motility of individual spermatozoa, which is an important characteristic for the quality assessment [37]. In ecoinformatics, motion features can be used to classify certain species, for instance birds [38]. In [39], [40], tracking has also been proposed to utilize motionbased features for the characterization of materials in sensorbased sorting.…”