Novelty detection with self-organizing maps for autonomous extraction of salient tracking features

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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https://hal.archives-ouvertes.fr/hal-02156627
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Submitted on : Friday, June 14, 2019 - 3:10:39 PM
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  • HAL Id : hal-02156627, version 1

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Yann Bernard, Nicolas Hueber, Bernard Girau. Novelty detection with self-organizing maps for autonomous extraction of salient tracking features. 13th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization, Jun 2019, Barcelona, Spain. ⟨hal-02156627⟩

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