During the Symposium organized by the British machine vision association (BMVA), focused on Human Activity Recognition and Monitoring, I had the chance to present the research work performed during my PhD studentship.
In particular, I’ve presented my work titled Social Activity Recognition from Continuous RGB-D Sequences.
I have performed this research work alongside my colleagues Sehran Cosar and Nicola Bellotto in collaboration with Diego R. Faria (Aston University).
Social Activity Recognition from Continuous RGB-D Sequences.
The presentation describes our approach to recognise social activities for indoor robots.
Therefore, it focuses on tracked skeletons data, in the case of untrimmed activity video clips.
The approach detects the temporal intervals of human social interactions to then recognise the type of social activities performed by a pair of persons.
This research is a continuation of my works recently presented at IROS 2016 and ROMAN 2017.
Furthermore, It includes the results of the experiments described in the paper submitted for revision to the Pattern Recognition Letters journal.
The event has been very interesting. Many important researchers were presenting their work on Human Activity Recognition from different contexts and aimed to a wide spectrum of applications.
The event was chaired by Ardhendu Behera (Edge Hill University), Nicola Bellotto (University of Lincoln) & Charith Abhayaratne (University of Sheffield).
It included Keynote speakers such as Prof David Hogg (University of Leeds), Dr Alessandro Vinciarelli (University of Glasgow), Prof Ian Craddock (University of Bristol) and Prof Yiannis Demiris (Imperial College London).
The full program of the event with all the presenters can be found here.
Here you can find the slides used for my presentation: