Dog Behavior Recognition and Tracking based on Faster R-CNN

Year 2020, Volume: 35 Issue: 2, 819 - 834, 25.12.2019

Emre Dandıl , Rukiye Polattimur

https://doi.org/10.17341/gazimmfd.541677

Cited By: 7

Abstract

Recently, detection and recognition of animal
faces, body postures, behaviors, and physical movements is became an
interdisciplinary field. Computer vision methods can contribute to determine
behaviors of animals and predict the following behavior of animals. Moreover, these
methods would contribute to domesticate animals. In this study, a deep learning
based system is proposed for the detection and classification of dog’s
behaviour. In the study, firstly, a dataset is created by collecting videos
containing the behavior of dogs which don’t avoid contact with people. After the
necessary analysis on the obtained videos, a customized data set consisting of
more meaningful sections is developed by extracting determined behaviors in
videos. It is recognized the behavior with the Faster R-CNN (Faster Regional-Convolutional
Neural Networks) by selecting key frames from these customized video sections.
In the last stage, the related behaviors in videos are followed by video tracker
after the behavior of the dog is recognized. As a result of experimental
studies, the behaviors of dog such as opening the mouth, sticking out the tongue,
sniffing, rearing the ear, swinging the tail and playing are examined and accuracy rates 94.00%, 98.00%,
99.33%, 99.33%, 98.00% and 98.67% are obtained for these behaviors,
respectively. With the results obtained in the study, it is seen that our proposed
method based on key frame selection and determination of regions of interest are
successful in recognition the behavior of dogs.

Keywords

Computer Vision, Deep Learning, Faster Regional-Convolutional Neural Networks (Faster R-CNN), Animal Behavior Analysis, Dog Behavior Recognition

Daha hızlı bölgesel evrişimsel sinir ağları ile köpek davranışlarının tanınması ve takibi

Abstract

Hayvan yüzlerinin, vücut duruşlarının,
davranışlarının ve fiziksel hareketlerinin tespiti ve tanınması son zamanlarda
disiplinlerarası bir alan olarak ön plana çıkmıştır. Bilgisayarlı görü yöntemi
ile hayvanların davranışlarının tespitine, sonraki davranışların öngörülmesine
ve hayvanların evcilleştirilmesine katkı sunabilir. Bu çalışmada, köpeklerin
davranışlarının tespit edilmesi ve sınıflandırılması için derin öğrenmeye
dayalı bir sistem önerilmiştir. Çalışmada öncelikle, insanlar ile temastan
kaçınmayan köpeklerin
davranışlarını içeren videolar toplanarak bir veri seti oluşturulmuştur. Elde
edilen videolar üzerinde gerekli analizler yapıldıktan sonra belirlenen
davranışlar videolardan çıkarılarak, daha anlamlı bölümlerden oluşan
özelleştirilmiş bir veri seti geliştirilmiştir. Bu anlamlı video bölümlerinden anahtar çerçeveler seçilerek Daha Hızlı
Bölgesel-Evrişimsel Sinir Ağları (DH B-ESA) ile davranışlar tanınmıştır. Son aşamada ise, köpeğin davranışı tanındıktan sonra, video
üzerinde ilgili davranışlar takipçi ile izlenmiştir. Yapılan deneysel
çalışmalar sonucunda, köpeklerin ağız
açma, dil çıkarma, koklama, kulak dikme, kuyruk sallama ve oyun oynama davranışları incelenmiş ve bu davranışlar
için sırasıyla %94.00, %98.00, %99.33, %99.33, %98.00, %98.67 doğruluk oranı
elde edilmiştir.
Çalışmada elde edilen sonuçlar ile anahtar çerçeve seçimi ve ilgi bölgelerin
belirlenmesine dayalı önerilen yöntemin, köpeklerin davranışlarını tanımada
başarılı olduğu görülmüştür.

Keywords

Bilgisayarlı Görü, Derin Öğrenme, Daha Hızlı Bölgesel-Evrişimsel Sinir Ağları, Hayvan Davranış Analizi, Köpek Davranışı Tanıma

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