Michał Kępski, PhD

Interdisciplinary Centre for Computational Modelling
Faculty of Mathematics and Natural Sciences
University of Rzeszow
1 Prof. S. Pigonia Street
35-310 Rzeszów, POLAND
mail
University of Rzeszow
Linkedin profile Google Scholar profile Github

Terminy konsultacji: kalendarz

Teaching

Image analysis & processing, Computer graphics & human-computer communication, Python programming

Field of interest

computer vision, image processing, action recognition

Currently cooperates with

Bogdan Kwolek (AGH University of Science and Technology) Homepage

Tomasz Krzeszowski (Rzeszow University of Technology) Homepage

Datasets

The UR FALL DETECTION DATASET is available here.

PhD Thesis

Kepski, M.: Detekcja upadku i wybranych akcji na sekwencjach obrazów cyfrowych, Akademia Górniczo-Hutnicza im. Stanisława Staszica, Kraków, 2016 [PDF]

Publications

  1. Kepski M. and Kwolek B,: Event-driven system for fall detection using body-worn accelerometer and depth sensor, IET Computer Vision, Volume 12, Issue 1, February 2018, Pages 48-58, ISSN 1751-9632 [5-year IF = 1.612]
  2. Kepski, M. and Kwolek, B.: Fall detection using body-worn accelerometer and depth maps acquired by active camera; Int. Conf. on Hybrid Artificial Intelligent Systems HAIS 2016, Lecture Notes in Computer Science, vol. 9648, Springer, Cham, 2016.
  3. Kwolek B. and Kepski M,: Fuzzy inference-based fall detection using kinect and body-worn accelerometer, Applied Soft Computing, Volume 40, March 2016, Pages 305-318, ISSN 1568-4946 [IF = 2.857]
  4. Kepski, M. and Kwolek, B.: Embedded system for fall detection using body-worn accelerometer and depth sensor; IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), pp. 755-759, IEEE, 2015 [PDF]
  5. Kwolek B. and Kepski M,: Improving fall detection by the use of depth sensor and accelerometer, Neurocomputing, Volume 168, 30 November 2015, Pages 637-645, ISSN 0925-2312 [IF = 2.083] [PDF]
  6. Kwolek, B. and Kepski, M.: Human fall detection on embedded platform using depth maps and wireless accelerometer; Computer Methods and Programs in Biomedicine, Volume 117, Issue 3, December 2014, pp. 489-501, ISSN 0169-2607 [IF = 1.897]
  7. Kepski, M. and Kwolek, B.: Detecting human falls with 3-axis accelerometer and depth sensor; Engineering in Medicine and Biology Society (EMBC), 36th Annual Int. Conf. of the IEEE, pp. 770-773, IEEE, 2014 [PDF]
  8. Kepski, M. and Kwolek, B.: Person Detection and Head Tracking to Detect Falls in Depth Maps; Int. Conf. on Computer Vision and Graphics, Lecture Notes in Computer Science, vol. 8671, Springer International Publishing, 2014 [PDF]
  9. Kepski, M. and Kwolek, B.: Fall Detection using Ceiling-mounted 3D Depth Camera; Int. Conf. on Computer Vision Theory and Applications VISAPP 2014, Volume 2 pp. 640-647, SciTePress 2014. [PDF]
  10. Kwolek, B. and Kepski, M.: Fall Detection Using Kinect Sensor and Fall Energy Image; Int. Conf. on Hybrid Artificial Intelligent Systems HAIS 2013, Lecture Notes in Computer Science, vol. 8073, Springer Berlin Heidelberg, 2013. [PDF]
  11. Kepski, M. and Kwolek, B.: Unobtrusive Fall Detection at Home Using Kinect Sensor; Int. Conf. on Computer Analysis of Images and Patterns CAIP 2013, Lecture Notes in Computer Science, vol. 8047, Springer Berlin Heidelberg, 2013. [PDF]
  12. Kepski, M. and Kwolek, B.: Human Fall Detection Using Kinect Sensor; Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013, Advances in Intelligent Systems and Computing, vol. 226, Springer International Publishing, 2013. [PDF][Presentation]
  13. Kepski M. and Kwolek B.: Human Fall Detection by Mean Shift Combined with Depth Connected Components; Int. Conf. on Computer Vision and Graphics, Lecture Notes in Computer Science, vol. 7594, Springer Berlin / Heidelberg, 2012.[Best Presentation Award] [PDF][Presentation]
  14. Kepski M. and Kwolek B.: Fall Detection on Embedded Platform Using Kinect and Wireless Accelerometer; Int. Conf. on Computers Helping People with Special Needs, Lecture Notes in Computer Science, vol. 7383, Springer Berlin / Heidelberg, 2012. [PDF][Presentation]
  15. Kepski M., Kwolek B. and Austvoll I.: Fuzzy Inference-Based Reliable Fall Detection Using Kinect and Accelerometer; Int. Conf. on Artificial Intelligence and Soft Computing, Lecture Notes in Computer Science, vol. 7267, Springer Berlin / Heidelberg, 2012. [PDF][Poster]