Journal
2014 22ND INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)
Volume -, Issue -, Pages 4310-4315Publisher
IEEE COMPUTER SOC
DOI: 10.1109/ICPR.2014.739
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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.
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