Abstract
Gender profiling is a fundamental task that helps CCTV systems to provide better service for intelligent surveillance. Since subjects being detected by CCTVs are not always cooperative, a few profiling algorithms are proposed to deal with situations when faces of subjects are not available, among which the most common approach is to analyze subjects’ body shape information. In addition, there are some drawbacks for normal profiling algorithms considered in real applications. First, the profiling result is always uncertain. Second, for a time-lasting gender profiling algorithm, the result is not stable. The degree of certainty usually varies, sometimes even to the extent that a male is classified as a female, and vice versa. These facets are studied in a recent paper [16] using Dempster-Shafer theory. In particular, Denoeux’s cautious rule is applied for fusion mass functions through time lines. However, this paper points out that if severe mis-classification is happened at the beginning of the time line, the result of applying Denoeux’s rule could be disastrous. To remedy this weakness, in this paper, we propose two generalizations to the DS approach proposed in [16] that incorporates time-window and time-attenuation, respectively, in applying Denoeux’s rule along with time lines, for which the DS approach is a special case. Experiments show that these two generalizations do provide better results than their predecessor when mis-classifications happen.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bsia. Florida school bus surveillance, http://www.bsia.co.uk/LY8VIM18989_action;displaystudy_sectorid;LYCQYL79312_caseid;NFLEN064798
Dempster, A.P.: Upper and lower probabilities induced by a multivalued mapping. The Annals of Statistics 28, 325–339 (1967)
Denœux, T.: Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence. Artifical Intelligence 172(2-3), 234–264 (2008)
Dubois, D., Prade, H., Yager, R.: Fuzzy set connectives as combinations of belief structures. Information Sciences 66, 245–275 (1992)
Konieczny, S., Pino-Pérez, R.: On the logic of merging. In: Cohn, A.G., Schubert, L., Shapiro, S.C. (eds.) Principles of Knowledge Representation and Reasoning, KR 1998, pp. 488–498. Morgan Kaufmann, San Francisco (1998)
Ma, J., Liu, W.: Modeling belief change on epistemic states. In: Proc. of 22th Flairs, pp. 553–558. AAAI Press (2009)
Ma, J., Liu, W.: A framework for managing uncertain inputs: An axiomization of rewarding. Inter. Journ. of Approx. Reasoning 52(7), 917–934 (2011)
Ma, J., Liu, W., Dubois, D., Prade, H.: Revision rules in the theory of evidence. In: Procs. of ICTAI, pp. 295–302 (2010)
Ma, J., Liu, W., Dubois, D., Prade, H.: Bridging jeffrey’s rule, agm revision and dempster conditioning in the theory of evidence. International Journal on Artificial Intelligence Tools 20(4), 691–720 (2011)
Ma, J., Liu, W., Hunter, A.: Incomplete Statistical Information Fusion and Its Application to Clinical Trials Data. In: Prade, H., Subrahmanian, V.S. (eds.) SUM 2007. LNCS (LNAI), vol. 4772, pp. 89–103. Springer, Heidelberg (2007)
Ma, J., Liu, W., Hunter, A.: The Non-archimedean Polynomials and Merging of Stratified Knowledge Bases. In: Sossai, C., Chemello, G. (eds.) ECSQARU 2009. LNCS, vol. 5590, pp. 408–420. Springer, Heidelberg (2009)
Ma, J., Liu, W., Hunter, A., Zhang, W.: Performing meta-analysis with incomplete statistical information in clinical trials. BMC Medical Research Methodology 8(1), 56 (2008)
Ma, J., Liu, W., Miller, P.: Event Modelling and Reasoning with Uncertain Information for Distributed Sensor Networks. In: Deshpande, A., Hunter, A. (eds.) SUM 2010. LNCS, vol. 6379, pp. 236–249. Springer, Heidelberg (2010)
Ma, J., Liu, W., Miller, P.: Belief change with noisy sensing in the situation calculus. In: Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), pp. 471–478 (2011)
Ma, J., Liu, W., Miller, P.: Handling Sequential Observations in Intelligent Surveillance. In: Benferhat, S., Grant, J. (eds.) SUM 2011. LNCS, vol. 6929, pp. 547–560. Springer, Heidelberg (2011)
Ma, J., Liu, W., Miller, P.: An evidential improvement for gender profiling. In: Denoeux, T., Masson, M. (eds.) Procs. of the Belief Functions: Theory and Applications, BELIEF 2012 (2012)
Ma, J., Liu, W., Miller, P., Yan, W.: Event composition with imperfect information for bus surveillance. In: Procs. of AVSS, pp. 382–387. IEEE Press (2009)
Miller, P., Liu, W., Fowler, F., Zhou, H., Shen, J., Ma, J., Zhang, J., Yan, W., McLaughlin, K., Sezer, S.: Intelligent sensor information system for public transport: To safely go.... In: Procs. of AVSS (2010)
ECIT Queen’s University of Belfast. Airport corridor surveillance (2010), http://www.csit.qub.ac.uk/Research/ResearchGroups/IntelligentSurveillanceSystems
US Department of Transportation. Rita - its research program (2010), http://www.its.dot.gov/ITS_ROOT2010/its_program/ITSfederal_program.htm
Gardiner Security. Glasgow transforms bus security with ip video surveillance, http://www.ipusergroup.com/doc-upload/Gardiner-Glasgowbuses.pdf
Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press (1976)
Smets, P.: Non-standard logics for automated reasoning. In: Smets, P., Mamdani, A., Dubois, D., Prade, H. (eds.) Belief Functions, pp. 253–286 (1988)
Yager, R.: On the dempster-shafer framework and new combination rules. Information Sciences 41, 93–138 (1987)
Zhou, H., Miller, P., Zhang, J., Collins, M., Wang, H.: Gender classification using facial and full body features. Technical Report, CSIT, Queen’s University Belfast, UK (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ma, J., Liu, W., Miller, P. (2012). Evidential Fusion for Gender Profiling. In: Hüllermeier, E., Link, S., Fober, T., Seeger, B. (eds) Scalable Uncertainty Management. SUM 2012. Lecture Notes in Computer Science(), vol 7520. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33362-0_39
Download citation
DOI: https://doi.org/10.1007/978-3-642-33362-0_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33361-3
Online ISBN: 978-3-642-33362-0
eBook Packages: Computer ScienceComputer Science (R0)