Abstract
This work proposes a human interaction recognition based approach to video indexing that represents a video by showing when and with whom was interacted throughout the video. In order to visualize the length of an interaction, it is required to recognize individuals that have been detected in earlier parts of the video. To solve this problem, an approach to photo-clustering is extended to video material by tracking detected faces and using the information from tracking to improve the recognition of human beings. The results of the tracking based approach show a considerable decrease of false cluster assignments compared to the original method. Further, it is demonstrated that the proposed method is able to correctly recognize the appearance of five out of the six individuals correctly.
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
References
Song, Y., Leung, T.: Context-aided human recognition - clustering. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) Computer Vision - ECCV 2006. LNCS, vol. 3953, pp. 382–395. Springer, Berlin Heidelberg (2006)
Everingham, M., Sivic, J., Zisserman, A.: Taking the bite out of automatic naming of characters in TV video. Image Vis. Comput. 27(5), 545–559 (2009)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features (2001)
Luxburg, U.: A tutorial on spectral clustering. Stat. Comput. 17(4), 395–416 (2007)
Aly, M., Munich, M., Perona, P.: Multiple dictionaries for bag of words large scale image search. In: International Conference on Image Processing (ICIP), Brussels, Belgium, September 2011
Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’s distance as a metric for image retrieval. Int. J. Comput. Vis. 40(2), 99–121 (2000)
Jensen, J.H., Ellis, D.P., Christensen, M.G., Jensen, S.H.: Evaluation distance measures between Gaussian mixture models of mfccs. In: ISMIR, (2011) Observation of Strains: Proceedings of the 8th International Conference on Music Information Retrieval, 23–27 September 2007, Vienna, Austria, pp. 107–108. Austrian Computer Society (2007)
Rand, W.M.: Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66(336), 846–850 (1971)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Meißner, F., Veltkamp, R.C. (2017). Monitoring Interactions. In: Poppe, R., Meyer, JJ., Veltkamp, R., Dastani, M. (eds) Intelligent Technologies for Interactive Entertainment. INTETAIN 2016 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 178. Springer, Cham. https://doi.org/10.1007/978-3-319-49616-0_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-49616-0_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-49615-3
Online ISBN: 978-3-319-49616-0
eBook Packages: Computer ScienceComputer Science (R0)