Novelty-based Spatiotemporal Saliency Detection for Prediction of Gaze in Egocentric Video

@article{Polatsek2016NoveltybasedSS,
  title={Novelty-based Spatiotemporal Saliency Detection for Prediction of Gaze in Egocentric Video},
  author={Patrik Polatsek and Wanda Benesova and Lucas Paletta and Roland Perko},
  journal={IEEE Signal Processing Letters},
  year={2016},
  volume={23},
  pages={394-398}
}
The automated analysis of video captured from a first-person perspective has gained increased interest since the advent of marketed miniaturized wearable cameras. With this a person is taking visual measurements about the world in a sequence of fixations which contain relevant information about the most salient parts of the environment and the goals of the actor. We present a novel model for gaze prediction in egocentric video based on the spatiotemporal visual information captured from the… CONTINUE READING

Figures, Results, and Topics from this paper.

Key Quantitative Results

  • Experimental results are gained from egocentric videos using eye-tracking glasses in a natural shopping task and prove a 6.48% increase in the mean saliency at a fixation in terms of a measure of mimicking human attention.
  • Experimental results also shows 6.48% increase in the mean saliency at a fixation in terms of a measure of mimicking human attention.

Citations

Publications citing this paper.
SHOWING 1-5 OF 5 CITATIONS

Digging Deeper Into Egocentric Gaze Prediction

  • 2019 IEEE Winter Conference on Applications of Computer Vision (WACV)
  • 2019

Going from Image to Video Saliency: Augmenting Image Salience with Dynamic Attentional Push

  • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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CITES BACKGROUND

Egocentric Meets Top-View

  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2016
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CITES BACKGROUND

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