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

Yann Bernard (1, 2) , Nicolas Hueber (1) , Bernard Girau (2)

1 ISL - Institut franco-allemand de recherches de Saint-Louis
2 BISCUIT - Bio-Inspired, Situated and Cellular Unconventional Information Technologies

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.

Mots clés: dynamic neural fields; saliency map; self-organizing maps

Domaines: Traitement des images [eess.IV]; Apprentissage [cs.LG]; Réseau de neurones [cs.NE]; Informatique [cs]

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. ⟨10.1007/978-3-030-19642-4_10⟩. ⟨hal-02156627⟩