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Spatio-temporal classification of images and video

Primary supervisor

Campbell Wilson


This project aims to identify novel methods for inferring where and when photographs and videos were recorded from features of the material itself. A key requirement of image processing in a Law Enforcement (LE) context is to augment classification of material by identifying its spatio-temporal context.

The Faculty of Information Technology has a mission to advance social good through its research. Key to this mission is the AiLECS (Artificial Intelligence for Law Enforcement and Community Safety) research lab. The AiLECS lab is a joint initiative of Monash University and the Australian Federal Police, and researches the ethical application of AI theories and techniques to problems of interest to law enforcement agencies. The work of the lab is applied in nature, we seek to rapidly translate our research into real-world solutions to significant threats to community safety.

In this case, the problem concerns not only inferring ‘is this an image of child abuse?’, but addressing ‘where and when was this image produced, ‘is this image part of a series’, and/or ‘have we seen an image from a similar place or time before’? This inference needs to be done by extraction and recognition of relevant  features from the content of the image/video itself, rather than metadata such as exif which may be missing, obscured, or incorrect.  How do we move beyond detecting features to inferring aspects of their meaning?






Required knowledge

This project will suit candidates with experience in machine learning, specifically  deep convolutional neural networks, with an interest in applied research. 

Project funding


Learn more about minimum entry requirements.