Abstract
In the image processing field, many tracking algorithms rely on prior knowledge like color, shape or even need a database of the objects to be tracked. This may be a problem for some real world applications that cannot fill those prerequisite. Based on image compression techniques, we propose to use Self-Organizing Maps to robustly detect novelty in the input video stream and to produce a saliency map which will outline unusual objects in the visual environment. This saliency map is then processed by a Dynamic Neural Field to extract a robust and continuous tracking of the position of the object. Our approach is solely based on unsupervised neural networks and does not need any prior knowledge, therefore it has a high adaptability to different inputs and a strong robustness to noisy environments.
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References
Amari S (1977) Dynamics of pattern formation in lateral-inhibition type neural fields. Biol Cybern 27:77
Amerijckx C, Legat J-D, Verleysen M (2003) Image compression using self-organizing maps. Syst Anal Model Simulat 43(11):1529–1543
Fix J, Rougier NP, Alexandre F (2011) A dynamic neural field approach to the covert and overt deployment of spatial attention. Cognit Comput 3(1):279–293
Kohonen T (1990) The self-organizing map. Proc IEEE 78(9):1464–1480
Kohonen T (2013) Essentials of the self-organizing map. Neural Netw 37:52–65
Kulchandani JS, Dangarwala KJ (2015) Moving object detection: review of recent research trends. In: International conference on pervasive computing (ICPC), Pune, pp 1–5
Lee H, Cho S (2005) SOM-based novelty detection using novel data. In: Gallagher M, Hogan JP, Maire F (eds) Intelligent data engineering and automated learning. Lecture notes in computer science, vol 3578. Springer, Heidelberg
Lotfi Shahreza M, Moazzami D, Moshiri B, Delavar MR (2011) Anomaly detection using a self-organizing map and particle swarm optimization. Sci Iran 18(6):1460–1468
Redmon J, Divvala SK, Girshick RB, Farhadi A (2015) You only look once: unified, real-time object detection. CoRR, abs/1506.02640
Rougier NP, Noelle DC, Braver TS, Cohen JD, O’Reilly RC (2005) Prefrontal cortex and flexible cognitive control: rules without symbols. PNAS 102(20):7338–7343
Taylor J (1999) Neural bubble dynamics in two dimensions: foundations. Biol Cybern 80:393–409
Vazquez R, Girau B, Quinton J-C (2011) Visual attention using spiking neural maps. In: International joint conference on neural networks IJCNN, San José
Vitay J, Rougier NP, Alexandre F (2005) A distributed model of spatial visual attention. In: Wermter S, Palm G, Elshaw M (eds) Biomimetic neural learning for intelligent robots. Lecture notes in computer science, vol 3575. Springer, Heidelberg
Wang Y, Jodoin P, Porikli F, Konrad J, Benezeth Y, Ishwar P (2014) CDnet 2014: an expanded change detection benchmark dataset In: IEEE conference on computer vision and pattern recognition workshops, Columbus, OH, pp 393–400
Wong MLD, Jack LB, Nandi AK (2006) Modified self-organising map for automated novelty detection applied to vibration signal monitoring. Mech Syst Signal Process 20(3):593–610
Xiao Y, Leung CS, Lam PM et al (2012) Self-organizing map-based color palette for high-dynamic range texture compression. Neural Comput Appl 21:639
Acknowledgements
The authors thank the French AID agency (Agence de l’Innovation pour la Défense) for funding the DGA-2018 60 0017 contract.
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Bernard, Y., Hueber, N., Girau, B. (2020). Novelty Detection with Self-Organizing Maps for Autonomous Extraction of Salient Tracking Features. In: Vellido, A., Gibert, K., Angulo, C., Martín Guerrero, J. (eds) Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization. WSOM 2019. Advances in Intelligent Systems and Computing, vol 976. Springer, Cham. https://doi.org/10.1007/978-3-030-19642-4_10
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DOI: https://doi.org/10.1007/978-3-030-19642-4_10
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