Paper Info
Title
First-Person Animal Activity Recognition from Egocentric Videos
Abstract
This paper introduces the concept of first-person animal activity recognition, the problem of recognizing activities from a view-point of an animal (e.g., a dog). Similar to first-person activity recognition scenarios where humans wear cameras, our approach estimates activities performed by an animal wearing a camera. This enables monitoring and understanding of natural animal behaviors even when there are no people around them. Its applications include automated logging of animal behaviors for medical/biology experiments, monitoring of pets, and investigation of wildlife patterns. In this paper, we construct a new dataset composed of first-person animal videos obtained by mounting a camera on each of the four pet dogs. Our new dataset consists of 10 activities containing a heavy/fair amount of ego-motion. We implemented multiple baseline approaches to recognize activities from such videos while utilizing multiple types of global/local motion features. Animal ego-actions as well as human-animal interactions are recognized with the baseline approaches, and we discuss experimental results.
Year
DOI
Venue
2014
10.1109/ICPR.2014.739
Pattern Recognition
Keywords
DocType
ISSN
image motion analysis,image recognition,video signal processing,animal view-point,automated logging,baseline approaches,biology experiments,egocentric videos,first-person animal activity recognition,global motion features,human-animal interactions,local motion features,medical experiments,multiple baseline approaches,natural animal behaviors,pet monitoring
Conference
1051-4651
Citations 
PageRank 
References 
21
0.87
6
Authors
4
Name
Order
Citations
PageRank
Yumi Iwashita121223.59
Asamichi Takamine2210.87
Ryo Kurazume362274.18
M. S. Ryoo4142949.18