Efficient motion estimation methods for fast recognition of activities of daily living

Stergios Poularakis, Konstantinos Avgerinakis, Alexia Briassouli*, Ioannis Kompatsiaris

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


This work proposes a framework for the efficient recognition of activities of daily living (ADLs), captured by static color cameras, applicable in real world scenarios. Our method reduces the computational cost of ADL recognition in both compressed and uncompressed domains by introducing system level improvements in State of-the-Art activity recognition methods. Faster motion estimation methods are employed to replace costly dense optical flow (OF) based motion estimation, through the use of fast block matching methods, as well as motion vectors, drawn directly from the compressed video domain (MPEG vectors). This results in increased computational efficiency, with minimal loss in terms of recognition accuracy. To prove the effectiveness of our approach, we provide an extensive, in-depthinvestigation of the trade-offs between computational cost, compression efficiency and recognition accuracy, tested on bench-mark and real-world ADL video datasets.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalSignal Processing-Image Communication
Publication statusPublished - Apr 2017
Externally publishedYes


  • Activity recognition
  • Block matching
  • Computational efficiency
  • MPEG video encoding
  • Motion estimation


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