Adaptive Video Subsampling for Energy-Efficient Object Detection

DIvya Mohan, Sameeksha Katoch, Suren Jayasuriya, Pavan Turaga, Andreas Spanias

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations


Energy-efficient computer vision is vitally important for embedded and mobile platforms where a longer battery life can allow increased deployment in the field. In image sensors, one of the primary causes of energy expenditure is the sampling and digitization process. Smart subsampling of the image-array in a manner that is task-specific, can result in significant savings of energy. We present an adaptive algorithm for video subsampling, which is aimed at enabling accurate object detection, while saving sampling energy. The approach utilizes objectness measures, which we show can be accurately estimated even from sub-sampled frames, and then uses that information to determine the adaptive sampling for the subsequent frame. We show energy savings of 18-67% with only a slight degradation in object detection accuracy in experiments. These results motivated us to further explore energy-efficient subsampling using advanced techniques such as, reinforcement learning and Kalman filtering. The experiments using these techniques are underway and provide ample support for adaptive subsampling as a promising avenue for embedded computer vision in the future.

Original languageEnglish (US)
Title of host publicationConference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781728143002
StatePublished - Nov 2019
Event53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 - Pacific Grove, United States
Duration: Nov 3 2019Nov 6 2019

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393


Conference53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
Country/TerritoryUnited States
CityPacific Grove


  • Energy-efficient computer vision
  • Image and Video Subsam-pling
  • Objectness

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications


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