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Human activity understanding

Primary supervisor

Qiuhong Ke



The mobility of people and actions they perform provides valuable insights into the management and optimization of the inbuilt environment. Camera-based solutions are simple, fast and cost-effective but might cause privacy concerns. In this project we aim to develop camera-based solutions using human skeletons and artificial intelligence models or algorithms to preserve privacy while being able to gain insights into the human activities within the inbuilt environment. 


Student cohort

Single Semester
Double Semester


The objectives include both the design of a solution architecture so as to be camera agnostic and computational scalable using edge computing infrastructure, as well as the development of suitable AI/ML algorithms to detect, predict and inform the users of the activities within the targeted areas. The project will be conducted using the digital infrastructure deployed in the Monash Smart Manufacturing Hub.

Required knowledge

computer vision

deep learning