Video selection for visual sensor networks: A motion-based ranking algorithm. Moretti, S., Mazzotti, M., & Chiani, M. In 2016 24th European Signal Processing Conference (EUSIPCO), pages 383-387, Aug, 2016.
Video selection for visual sensor networks: A motion-based ranking algorithm [pdf]Paper  doi  abstract   bibtex   
A Visual Sensor Network (VSN) is composed by several cameras, in general with different characteristics and orientations, which are used to cover a certain Area of Interest (AoI). To provide an optimal and autonomous exploitation of the VSN video streams, suitable algorithms are needed for selecting the cameras capable to guarantee the best video quality for the specific AoI in the scene. In this work, a novel content and context-aware camera ranking algorithm is proposed, with the goal to maximize the Quality of Experience (QoE) to the final user. The proposed algorithm takes into account the pose, camera resolution and frame rate, and the quantity of motion in the scene. Subjective tests are performed to compare the ranking of the algorithm with human ranking. Finally, the proposed ranking algorithm is compared with common objective video quality metrics and a previous ranking algorithm, confirming the validity of the approach.
@InProceedings{7760275,
  author = {S. Moretti and M. Mazzotti and M. Chiani},
  booktitle = {2016 24th European Signal Processing Conference (EUSIPCO)},
  title = {Video selection for visual sensor networks: A motion-based ranking algorithm},
  year = {2016},
  pages = {383-387},
  abstract = {A Visual Sensor Network (VSN) is composed by several cameras, in general with different characteristics and orientations, which are used to cover a certain Area of Interest (AoI). To provide an optimal and autonomous exploitation of the VSN video streams, suitable algorithms are needed for selecting the cameras capable to guarantee the best video quality for the specific AoI in the scene. In this work, a novel content and context-aware camera ranking algorithm is proposed, with the goal to maximize the Quality of Experience (QoE) to the final user. The proposed algorithm takes into account the pose, camera resolution and frame rate, and the quantity of motion in the scene. Subjective tests are performed to compare the ranking of the algorithm with human ranking. Finally, the proposed ranking algorithm is compared with common objective video quality metrics and a previous ranking algorithm, confirming the validity of the approach.},
  keywords = {cameras;image motion analysis;optimisation;quality of experience;video cameras;video signal processing;video streaming;wireless sensor networks;video selection;visual sensor network;motion-based ranking algorithm;area of interest;AoI;VSN video stream;context-aware camera ranking algorithm;quality of experience maximization;QoE maximization;camera resolution;frame rate;motion quantity;Cameras;Signal processing algorithms;Three-dimensional displays;Solid modeling;Visualization;Heuristic algorithms;Optical imaging;Visual Sensor Networks;QoE;camera selection techniques;ranking algorithms},
  doi = {10.1109/EUSIPCO.2016.7760275},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2016/papers/1570256221.pdf},
}
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