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Human activity recognition has been an open problem in computer vision for almost two decades. In that time there
have been many approaches proposed to solve this problem, but very few have managed to solve it in a way that is
sufficiently computationally efficient for real time applications. Recently this has changed, with keypoint based
methods demonstrating a high degree of accuracy with low computational cost. These approaches take a given
image and return a set of joint locations for each individual within an image. In order to achieve real time
performance, a sparse representation of these features over a given time frame is required for classification. Previous
methods have achieved this by using a reduced number of keypoints, but this approach gives a less robust
representation of the individual’s body pose and may limit the types of activity that can be detected. We present a
novel method for reducing the size of the feature set, by calculating the Euclidian distance and the direction of
keypoint changes across a number of frames. This allows for a meaningful representation of the individuals
movements over time. We show that this method achieves accuracy on par with current state of the art methods,
while demonstrating real time performance.
Original languageEnglish
Publication statusAccepted/In press - 9 Feb 2021
EventInternational Conference on image processing and vision engineering: International Conference on image processing and vision engineering - Online Virtual
Duration: 27 Apr 202130 Apr 2021


ConferenceInternational Conference on image processing and vision engineering
Abbreviated titleIMPROVE
Internet address


  • human activity recognition
  • computer vision
  • pose estimation
  • social signal processing

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