PhD Forum Abstract: Activity Classification at the Edge

被引:4
|
作者
Hosseininoorbin, Seyedehfaezeh [1 ,2 ]
机构
[1] Univ Queensland, Brisbane, Qld, Australia
[2] CSIRO, DATA61, Brisbane, Qld, Australia
关键词
Activity classification; Behaviour model; Deep learning; Time-frequency distribution; Tensor Processing Unit;
D O I
10.1109/IPSN48710.2020.00005
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This study investigates activity classification using deep learning for developing an energy efficient and high-performance behavioural model on edge (resource constrained) devices. The specific focus of this research is on cattle activity classification. Considering accuracy and efficiency of approaches, different deep neural network structures and data representations will be explored. The developed model will be capable of identifying multiple cattle behavioural patterns accurately. However, there are some challenges to develop energy efficient and computationally light systems.
引用
收藏
页码:369 / 370
页数:2
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