Abstract
This paper deals with the selection of relevant motion within a scene. The proposed method is based on 3D features extraction and their rarity quantification to compute bottom-up saliency maps. We show that the use of 3D motion features namely the motion direction and velocity is able to achieve much better results than the same algorithm using only 2D information. This is especially true in close scenes with small groups of people or moving objects and frontal view. The proposed algorithm uses motion features but it can be easily generalized to other dynamic or static features. It is implemented on a platform for real-time signal analysis called Max/Msp/Jitter. Social signal processing, video games, gesture processing and, in general, higher level scene understanding can benefit from this method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Mancas, M.: Relative influence of bottom-up and top-down attention. In: Paletta, L., Tsotsos, J.K. (eds.) WAPCV 2008. LNCS, vol. 5395, pp. 212–226. Springer, Heidelberg (2009)
Dhavale, N., Itti, L.: Saliency-based multifoveated MPEG compression. In: Proceedings of Signal Processing and Its Applications, pp. 229–232 (2003)
Tsotsos, J.K., Liu, Y., Martinez-Trujillo, J.C., Pomplun, M., Simine, E., Zhou, K.: Attenting to visual motion. J. of Computer Vision and Image Understandig (2005)
Parkhurst, D.J., Niebur, E.: Texture contrast attracts overt visual attention in natural scenes. European Journal of Neuroscience 19(3), 783–789 (2004)
Itti, L., Baldi, P.: Bayesian Surprise Attracts Human Attention. Advances in Neural Information Processing Systems 18, 547 (2006)
Le Meur, O., Le Callet, P., Barba, D., Thoreau, D.: A Coherent Computational Approach to Model Bottom-Up Visual Attention. PAMI, 802–817 (2006)
Bruce, N.D.B., Tsotsos, J.K.: Saliency, attention, and visual search: an information theoretic approach. Journal of Vision 9(3), 5 (2009)
Boiman, O., Irani, M.: Detecting Irregularities in Images and in Video. International Journal of Computer Vision 74(1), 17–31 (2007)
Ouerhani, N., Huegli, H.: Computing visual attention from scene depth. In: Proc. of Int’l Conf. on Pattern Recognition, vol. 1 (2000)
Microsoft Kinect sensor, http://www.xbox.com/kinect
Max MSP, http://cycling74.com
FTM library, http://ftm.ircam.fr/index.php/Main_Page
Farnebäck, G.: Two-Frame Motion Estimation Based on Polynomial Expansion. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 363–370. Springer, Heidelberg (2003)
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
Riche, N., Mancas, M., Gosselin, B., Dutoit, T. (2011). 3D Saliency for Abnormal Motion Selection: The Role of the Depth Map. In: Crowley, J.L., Draper, B.A., Thonnat, M. (eds) Computer Vision Systems. ICVS 2011. Lecture Notes in Computer Science, vol 6962. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23968-7_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-23968-7_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23967-0
Online ISBN: 978-3-642-23968-7
eBook Packages: Computer ScienceComputer Science (R0)