Prediction-based Load Control and Balancing for Feature Extraction in Visual Sensor Networks
2014 (English)Conference paper (Refereed)
We consider controlling and balancing the processing load in a visual sensor network (VSN) used for detecting local features, such as BRISK. We formulate a prediction problem with random missing data, and propose two regression-based algorithms for data reconstruction. Numerical results illustrate the performance of the proposed algorithms, and show that backward regression combined with the last value predictor can be used for controlling and balancing the processing load in VSNs with good performance.
Place, publisher, year, edition, pages
2014. 674-678 p.
, International Conference on Acoustics Speech and Signal Processing ICASSP, ISSN 1520-6149
IdentifiersURN: urn:nbn:se:kth:diva-156174DOI: 10.1109/ICASSP.2014.6853681ISI: 000343655300136ScopusID: 2-s2.0-84905241294ISBN: 978-1-4799-2893-4ISBN: 978-147992892-7OAI: oai:DiVA.org:kth-156174DiVA: diva2:765483
2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014; Florence; Italy; 4 May 2014 through 9 May 2014
FunderEU, FP7, Seventh Framework Programme
QC 201501232014-11-242014-11-242015-01-23Bibliographically approved