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Motion priors based on goals hierarchies in pedestrian tracking applications / Francisco Madrigal in Machine Vision and Applications, vol 28 n° 3-4 (May 2017)  

Public [article]  inMachine Vision and Applications > vol 28 n° 3-4 (May 2017) . - pp 341 - 359 Titre : Motion priors based on goals hierarchies in pedestrian tracking applications Type de document : Article/Communication Auteurs : Francisco Madrigal, Auteur ; Jean-Bernard Hayet, Auteur Année de publication : 2017 Article en page(s) : pp 341 - 359 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] carrefour [Termes IGN] compréhension de l'image [Termes IGN] image vidéo [Termes IGN] modèle de simulation [Termes IGN] position [Termes IGN] poursuite de cible [Termes IGN] prévision [Termes IGN] réalité de terrain [Termes IGN] séquence d'images Résumé : (auteur) In this paper, the problem of automated scene understanding by tracking and predicting paths for multiple humans is tackled, with a new methodology using data from a single, fixed camera monitoring the environment. Our main idea is to build goal-oriented prior motion models that could drive both the tracking and path prediction algorithms, based on a coarse-to-fine modeling of the target goal. To implement this idea, we use a dataset of training video sequences with associated ground-truth trajectories and from which we extract hierarchically a set of key locations. These key locations may correspond to exit/entrance zones in the observed scene, or to crossroads where trajectories have often abrupt changes of direction. A simple heuristic allows us to make piecewise associations of the ground-truth trajectories to the key locations, and we use these data to learn one statistical motion model per key location, based on the variations of the trajectories in the training data and on a regularizing prior over the models spatial variations. We illustrate how to use these motion priors within an interacting multiple model scheme for target tracking and path prediction, and we finally evaluate this methodology with experiments on common datasets for tracking algorithms comparison. Numéro de notice : A2017-325 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00138-017-0832-8 Date de publication en ligne : 15/03/2017 En ligne : http://doi.org/10.1007/s00138-017-0832-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85384 [article]