Abstract
A novel method for distinguishing classes of viewers from their aggregated eye movements is described. The probabilistic framework accumulates uniformly sampled gaze as Gaussian point spread functions (heatmaps), and measures the distance of unclassified scanpaths to a previously classified set (or sets). A similarity measure is then computed over the scanpath durations. The approach is used to compare human observers’s gaze over video to regions of interest (ROIs) automatically predicted by a computational saliency model. Results show consistent discrimination between human and artificial ROIs, regardless of either of two differing instructions given to human observers (free or tasked viewing).
Keywords
- Video Sequence
- Video Frame
- Human Observer
- Saliency Model
- Dynamic Medium
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, access via your institution.
Buying options
Preview
Unable to display preview. Download preview PDF.
References
Land, M.F., Tatler, B.W.: Looking and Acting: Vision and Eye Movements in Natural Behavior. Oxford University Press, New York (2009)
Smith, T.J., Henderson, J.M.: Edit Blindness: The Relationship Between Attention and Global Change Blindness in Dynamic Scenes. Journal of Eye Movement Research 2, 1–17 (2008)
Franchak, J.M., Kretch, K.S., Soska, K.C., Babcock, J.S., Adolph, K.E.: Head-Mounted Eye-Tracking of Infants’ Natural Interactions: A New Method. In: ETRA 2010: Proceedings of the 2010 Symposium on Eye Tracking Research & Applications, pp. 21–27. ACM, New York (2010)
d’Ydewalle, G., Desmet, G., Van Rensbergen, J.: Film perception: The processing of film cuts. In: Underwood, G.D.M. (ed.) Eye guidance in reading and scene perception, pp. 357–367. Elsevier Science Ltd., Oxford (1998)
Grindinger, T., Duchowski, A.T., Sawyer, M.: Group-Wise Similarity and Classification of Aggregate Scanpaths. In: ETRA 2010: Proceedings of the 2010 Symposium on Eye Tracking Research & Applications, pp. 101–104. ACM, New York (2010)
Yarbus, A.L.: Eye Movements and Vision. Plenum Press, New York (1967)
Privitera, C.M., Stark, L.W.: Algorithms for Defining Visual Regions-of-Interest: Comparison with Eye Fixations. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 22, 970–982 (2000)
Jarodzka, H., Holmqvist, K., Nyström, M.: A Vector-Based, Multidimensional Scanpath Similarity Measure. In: ETRA 2010: Proceedings of the 2010 Symposium on Eye Tracking Research & Applications, pp. 211–218. ACM, New York (2010)
Duchowski, A.T., Driver, J., Jolaoso, S., Ramey, B.N., Tan, W., Robbins, A.: Scanpath Comparison Revisited. In: ETRA 2010: Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications, pp. 219–226. ACM, New York (2010)
Pomplun, M., Ritter, H., Velichkovsky, B.: Disambiguating Complex Visual Information: Towards Communication of Personal Views of a Scene. Perception 25, 931–948 (1996)
Wooding, D.S.: Fixation Maps: Quantifying Eye-Movement Traces. In: ETRA 2002: Proceedings of the 2002 Symposium on Eye Tracking Research & Applications, pp. 31–36. ACM, New York (2002)
Hembrooke, H., Feusner, M., Gay, G.: Averaging Scan Patterns and What They Can Tell Us. In: ETRA 2006: Proceedings of the 2006 Symposium on Eye Tracking Research & Applications, p. 41. ACM, New York (2006)
Dempere-Marco, L., Hu, X.P., Ellis, S.M., Hansell, D.M., Yang, G.Z.: Analysis of Visual Search Patterns With EMD Metric in Normalized Anatomical Space. IEEE Transactions on Medical Imaging 25, 1011–1021 (2006)
Torstling, A.: The Mean Gaze Path: Information Reduction and Non-Intrusive Attention Detection for Eye Tracking. Master’s thesis, The Royal Institute of Technology, Stockholm, Sweden, Techreport XR-EE-SB 2007:008 (2007)
Airola, A., Pahikkala, T., Waegeman, W., De Baets, B., Salakoski, T.: A Comparison of AUC Estimators in Small-Sample Studies. In: Proceedings of the 3rd International workshop on Machine Learning in Systems Biology, pp. 15–23 (2009)
Paris, S., Durand, F.: A Fast Approximation of the Bilateral Filter using a Signal Processing Approach. Technical Report MIT-CSAIL-TR-2006-073, Massachusetts Institute of Technology (2006)
Itti, L., Koch, C., Niebur, E.: A Model of Saliency-Based Visual Attention for Rapid Scene Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 20, 1254–1259 (1998)
Leigh, R.J., Zee, D.S.: The Neurology of Eye Movements, 2nd edn. Contemporary Neurology Series. F. A. Davis Company, Philadelphia (1991)
Grindinger, T.J.: Event-Driven Similarity and Classification of Scanpaths. PhD thesis, Clemson University, Clemson, SC (2010)
Peters, R.J., Itti, L.: Computational Mechanisms for Gaze Direction in Interactive Visual Environments. In: ETRA 2006: Proceedings of the 2006 Symposium on Eye Tracking Research & Applications, pp. 27–32. ACM, New York (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Grindinger, T.J., Murali, V.N., Tetreault, S., Duchowski, A.T., Birchfield, S.T., Orero, P. (2011). Algorithm for Discriminating Aggregate Gaze Points: Comparison with Salient Regions-Of-Interest. In: Koch, R., Huang, F. (eds) Computer Vision – ACCV 2010 Workshops. ACCV 2010. Lecture Notes in Computer Science, vol 6468. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22822-3_39
Download citation
DOI: https://doi.org/10.1007/978-3-642-22822-3_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22821-6
Online ISBN: 978-3-642-22822-3
eBook Packages: Computer ScienceComputer Science (R0)