Towards Real time activity recognition

Research output: Contribution to conferencePaperpeer-review

2 Citations (Scopus)
179 Downloads (Pure)


Abstract—Activity recognition relates to the automatic visual
detection and interpretation of human behaviour and is emerging
as an active domain of computer vision. It has important
applications such as identifying individuals who are at risk of
suicide in public locations such as bridges or railway stations.
These individuals are known to exhibit easily observable activities
and behaviours such as pacing, looking up and down the railway
tracks, and leaving objects on the platform. In order to detect these
behaviours, an approach to individual person activity recognition
is needed which can run in real time and monitor multiple
individuals in parallel. We present a method for human activity
recognition using skeletal keypoints and investigate how using
varying sample rates and sequence lengths impacts accuracy. The
results show that for any given sequence length, optimising the
sample rate can result in an overall increase in classification
accuracy and improvement in run-time. Results demonstrate that
finding the optimal time period over which to sample frames is
more important than simply decreasing the number of frames
sampled. Further, we show that keypoint based activity
recognition approaches outperform other state of the art
approaches. Finally, we show that this approach is fast enough for
real time activity recognition when up to 14 people are present in
the image whilst maintaining a high degree of accuracy.
Original languageEnglish
Number of pages6
Publication statusPublished (in print/issue) - 1 Feb 2021
EventFourth IEEE International Conference on Image Processing, Applications and Systems - Genova, Italy (Virtual), Genova, Italy
Duration: 9 Dec 202011 Dec 2020


ConferenceFourth IEEE International Conference on Image Processing, Applications and Systems
Abbreviated titleIPAS 2020
Internet address

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Copyright 2021 Elsevier B.V., All rights reserved.


  • activity recognition
  • Human action recognition
  • human activity recognition
  • social signal processing
  • Real time systems
  • Video processing
  • Keypoints
  • Activity recognition
  • Real time


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