Improving the Accuracy of Intelligent Pose Estimation Systems Through Low Level Image Processing Operations

Jannik Christian Lærkegård Pedersen, Mattias Foltmar Sander, Niklas Fruerlund Jensen, Jonas Lasham Lakhrissi, Mikkel Gede Hansen, Patrick Staalbo, Andreas Wulff-Abramsson

Publikation: Konferencebidrag uden forlag/tidsskriftPaper uden forlag/tidsskriftForskning


The development of powerful and popular machine learning driven pose estimation systems have been on the rise during the past years. In this research we have investigated how the accuracy level can be increased by applying low level image processing techniques unto the footage before they are submitted to the pose estimation system. The techniques used were high and low contrast, histogram equalization, sharpness and canny edge detection. By applying them on datasets, containing different environments and lighting conditions the system’s accuracy was increased, ranging from 0.29% increase to 38.37% increase dependent on the context. These increases have potential to upgrade the pose estimation system to be less lighting sensitive.
Publikationsdato4 maj 2019
Antal sider4
StatusUdgivet - 4 maj 2019
BegivenhedInternational Conference on Digital Image & Signal Processing (DISP’19) - Oxford, Storbritannien
Varighed: 29 apr. 201930 apr. 2019


KonferenceInternational Conference on Digital Image & Signal Processing (DISP’19)


  • OpenPose
  • Image processing
  • Limb Estimation
  • Histogram equalization
  • Low-.level operations
  • Image Contrast
  • Sharpness
  • Canny edge detection