Abstract
CCTV systems are broadly deployed in the present world. Despite this, the impact on anti-social and criminal behaviour has been minimal. Subject reacquisition is a fundamental task to ensure in-time reaction for intelligent surveillance. However, traditional reacquisition based on face recognition is not scalable, hence in this paper we use reasoning techniques to reduce the computational effort which deploys the time-of-flight information between interested zones such as airport security corridors. Also, to improve accuracy of reacquisition, we introduce the idea of revision as a method of post-processing. We demonstrate the significance and usefulness of our framework with an experiment which shows much less computational effort and better accuracy.
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
Arth, C., Leistner, C., Bischof, H.: Object reacquisition and tracking in large-scale smart camera networks. In: Procs. of 1st ACM/IEEE Distributed Smart Cameras, ICDSC 2007, pp. 156–163 (2007)
Campbell-West, F., Wang, H., Miller, P.: Where is it? object reacquisition in surveillance video. In: Procs. of Machine Vision and Image Processing, IMVIP 2008, pp. 182–187 (2008)
Dempster, A.P.: A generalization of bayesian inference. J. Roy. Statist. Soc. Series B 30, 205–247 (1968)
Jiang, X., Fan, P., Ravyse, I., Sahli, H., Huang, J., Zhao, R., Zhang, Y.: Perception-based lighting adjustment of image sequences. In: Zha, H., Taniguchi, R.-i., Maybank, S. (eds.) ACCV 2009, Part III. LNCS, vol. 5996, pp. 118–129. Springer, Heidelberg (2010)
Liu, W., Miller, P., Ma, J., Yan, W.: Challenges of distributed intelligent surveillance system with heterogenous information. In: Procs. of QRASA, Pasadena, California, pp. 69–74 (2009)
Luxand.com. face recognition
Ma, J.: Qualitative approach to bayesian networks with multiple causes. IEEE Transactions on Systems, Man, and Cybernetics, Part A 42(2), 382–391 (2012)
Ma, J., Benferhat, S., Liu, W.: Revising partial pre-orders with partial pre-orders: A unit-based revision framework (2012)
Ma, J., Benferhat, S., Liu, W.: Revision over partial pre-orders: A postulational study. In: Hüllermeier, E., Link, S., Fober, T., Seeger, B. (eds.) SUM 2012. LNCS, vol. 7520, pp. 219–232. Springer, Heidelberg (2012)
Ma, J., Liu, W.: A general model for epistemic state revision using plausibility measures. In: Procs. of ECAI, pp. 356–360 (2008)
Ma, J., Liu, W.: Modeling belief change on epistemic states. In: Procs. of FLAIRS (2009)
Ma, J., Liu, W.: A framework for managing uncertain inputs: An axiomization of rewarding. Int. J. Approx. Reasoning 52(7), 917–934 (2011)
Ma, J., Liu, W., Benferhat, S.: A belief revision framework for revising epistemic states with partial epistemic states. In: Procs. of AAAI, pp. 333–338 (2010)
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.: Inducing probability distributions from knowledge bases with (in)dependence relations. In: Procs. of AAAI, pp. 339–344 (2010)
Ma, J., Liu, W., Hunter, A.: Modeling and reasoning with qualitative comparative clinical knowledge. Int. J. Intell. Syst. 26(1), 25–46 (2011)
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: Procs. of UAI (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.: A characteristic function approach to inconsistency measures for knowledge bases. In: Hüllermeier, E., Link, S., Fober, T., Seeger, B. (eds.) SUM 2012. LNCS, vol. 7520, pp. 473–485. Springer, Heidelberg (2012)
Ma, J., Liu, W., Miller, P.: Evidential fusion for gender profiling. In: Hüllermeier, E., Link, S., Fober, T., Seeger, B. (eds.) SUM 2012. LNCS, vol. 7520, pp. 514–524. Springer, Heidelberg (2012)
Ma, J., Liu, W., Miller, P.: An evidential improvement for gender profiling. In: Denœux, T., Masson, M.-H. (eds.) Belief Functions: Theory & Appl. AISC, vol. 164, pp. 29–36. Springer, Heidelberg (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)
Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press (1976)
Viola, P., Jones, M.J.: Rapid object detection using a boosted cascade of simple features. In: Procs. of IEEE CVPR, pp. 511–518 (2001)
Walter, M.R., Friedman, Y., Antone, M., Teller, S.: Appearance-based object reacquisition for mobile manipulation. In: Procs. of IEEE Computer Vision and Pattern Recognition Workshops, CVPRW, pp. 1–8 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ma, J., Liu, W., Miller, P., Campbell-West, F. (2013). An Improvement of Subject Reacquisition by Reasoning and Revision. In: Liu, W., Subrahmanian, V.S., Wijsen, J. (eds) Scalable Uncertainty Management. SUM 2013. Lecture Notes in Computer Science(), vol 8078. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40381-1_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-40381-1_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40380-4
Online ISBN: 978-3-642-40381-1
eBook Packages: Computer ScienceComputer Science (R0)