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Analysis of pedestrian movements and gestures using an on-board camera to predict their intentions / Joseph Gesnouin (2022)
Titre : Analysis of pedestrian movements and gestures using an on-board camera to predict their intentions Titre original : Analyse des mouvements et gestes des piétons via caméra embarquée pour la prédiction de leurs intentions Type de document : Thèse/HDR Auteurs : Joseph Gesnouin, Auteur ; Fabien Moutarde, Directeur de thèse Editeur : Paris : Université Paris Sciences et Lettres Année de publication : 2022 Importance : 171 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de doctorat de l'Université Paris Sciences et Lettres, Préparée à MINES ParisTech, Spécialité
Informatique temps réel, robotique et automatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage profond
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par réseau neuronal récurrent
[Termes IGN] estimation de pose
[Termes IGN] image RVB
[Termes IGN] instrument embarqué
[Termes IGN] navigation autonome
[Termes IGN] piéton
[Termes IGN] reconnaissance de gestes
[Termes IGN] réseau neuronal de graphes
[Termes IGN] squelettisation
[Termes IGN] trajectoire (véhicule non spatial)
[Termes IGN] vision par ordinateurIndex. décimale : THESE Thèses et HDR Résumé : (auteur) The autonomous vehicle (AV) is a major challenge for the mobility of tomorrow. Progress is being made every day to achieve it; however, many problems remain to be solved to achieve a safe outcome for the most vulnerable road users (VRUs). One of the major challenge faced by AVs is the ability to efficiently drive in urban environments. Such a task requires interactions between autonomous vehicles and VRUs to resolve traffic ambiguities. In order to interact with VRUs, AVs must be able to understand their intentions and predict their incoming actions. In this dissertation, our work revolves around machine learning technology as a way to understand and predict human behaviour from visual signals and more specifically pose kinematics. Our goal is to propose an assistance system to the AV that is lightweight, scene-agnostic that could be easily implemented in any embedded devices with real-time constraints. Firstly, in the gesture and action recognition domain, we study and introduce different representations for pose kinematics, based on deep learning models as a way to efficiently leverage their spatial and temporal components while staying in an euclidean grid-space. Secondly, in the autonomous driving domain, we show that it is possible to link the posture, the walking attitude and the future behaviours of the protagonists of a scene without using the contextual information of the scene (zebra crossing, traffic light...). This allowed us to divide by a factor of 20 the inference speed of existing approaches for pedestrian intention prediction while keeping the same prediction robustness. Finally, we assess the generalization capabilities of pedestrian crossing predictors and show that the classical train-test sets evaluation for pedestrian crossing prediction, i.e., models being trained and tested on the same dataset, is not sufficient to efficiently compare nor conclude anything about their applicability in a real-world scenario. To make the research field more sustainable and representative of the real advances to come. We propose new protocols and metrics based on uncertainty estimates under domain-shift in order to reach the end-goal of pedestrian crossing behavior predictors: vehicle implementation. Note de contenu : 1- Introduction
2- Human activity recognition with pose-driven deep learning models
3- From action recognition to pedestrian discrete intention prediction
4- Assessing the generalization of pedestrian crossing predictors
5- ConclusionNuméro de notice : 24066 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique temps réel, robotique et automatique : Paris Sciences et Lettres : 2022 DOI : sans En ligne : https://tel.hal.science/tel-03813520 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102091 Identifying map users with eye movement data from map-based spatial tasks: user privacy concerns / Hua Liao in Cartography and Geographic Information Science, vol 49 n° 1 (January 2022)
[article]
Titre : Identifying map users with eye movement data from map-based spatial tasks: user privacy concerns Type de document : Article/Communication Auteurs : Hua Liao, Auteur ; Weihua Dong, Auteur ; Zhicheng Zhan, Auteur Année de publication : 2022 Article en page(s) : pp 50 - 69 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] comportement
[Termes IGN] confidentialité
[Termes IGN] identité
[Termes IGN] lecture de carte
[Termes IGN] oculométrie
[Termes IGN] orientation
[Termes IGN] partage de données localisées
[Termes IGN] protection de la vie privée
[Termes IGN] utilisateur
[Termes IGN] visualisation cartographique
[Vedettes matières IGN] CartologieRésumé : (auteur) Individuals with different characteristics exhibit different eye movement patterns in map reading and wayfinding tasks. In this study, we aim to explore whether and to what extent map users’ eye movements can be used to detect who created them. Specifically, we focus on the use of gaze data for inferring users’ identities when users are performing map-based spatial tasks. We collected 32 participants’ eye movement data as they utilized maps to complete a series of self-localization and spatial orientation tasks. We extracted five sets of eye movement features and trained a random forest classifier. We used a leave-one-task-out approach to cross-validate the classifier and achieved the best identification rate of 89%, with a 2.7% equal error rate. This result is among the best performances reported in eye movement user identification studies. We evaluated the feature importance and found that basic statistical features (e.g. pupil size, saccade latency and fixation dispersion) yielded better performance than other feature sets (e.g. spatial fixation densities, saccade directions and saccade encodings). The results open the potential to develop personalized and adaptive gaze-based map interactions but also raise concerns about user privacy protection in data sharing and gaze-based geoapplications. Numéro de notice : A2022-018 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2021.1980435 Date de publication en ligne : 06/10/2021 En ligne : https://doi.org/10.1080/15230406.2021.1980435 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99161
in Cartography and Geographic Information Science > vol 49 n° 1 (January 2022) . - pp 50 - 69[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 032-2022011 RAB Revue Centre de documentation En réserve L003 Disponible Pedestrian trajectory prediction with convolutional neural networks / Simone Zamboni in Pattern recognition, vol 121 (January 2022)
[article]
Titre : Pedestrian trajectory prediction with convolutional neural networks Type de document : Article/Communication Auteurs : Simone Zamboni, Auteur ; Zekarias Tilahun Kefato, Auteur ; Sarunas Girdzijauskas, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 108252 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par réseau neuronal récurrent
[Termes IGN] distance euclidienne
[Termes IGN] filtre de Gauss
[Termes IGN] itinéraire piétionnier
[Termes IGN] modèle de simulation
[Termes IGN] navigation pédestre
[Termes IGN] piéton
[Termes IGN] prévision à court terme
[Termes IGN] réseau social
[Termes IGN] trajet (mobilité)Résumé : (auteur) Predicting the future trajectories of pedestrians is a challenging problem that has a range of application, from crowd surveillance to autonomous driving. In literature, methods to approach pedestrian trajectory prediction have evolved, transitioning from physics-based models to data-driven models based on recurrent neural networks. In this work, we propose a new approach to pedestrian trajectory prediction, with the introduction of a novel 2D convolutional model. This new model outperforms recurrent models, and it achieves state-of-the-art results on the ETH and TrajNet datasets. We also present an effective system to represent pedestrian positions and powerful data augmentation techniques, such as the addition of Gaussian noise and the use of random rotations, which can be applied to any model. As an additional exploratory analysis, we present experimental results on the inclusion of occupancy methods to model social information, which empirically show that these methods are ineffective in capturing social interaction. Numéro de notice : A2022-109 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.patcog.2021.108252 Date de publication en ligne : 13/08/2021 En ligne : https://doi.org/10.1016/j.patcog.2021.108252 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99615
in Pattern recognition > vol 121 (January 2022) . - n° 108252[article]Simulation of dispersion effects by considering interactions of pedestrians and bicyclists using an agent space model / Mingwei Liu in Computers, Environment and Urban Systems, vol 91 (January 2022)
[article]
Titre : Simulation of dispersion effects by considering interactions of pedestrians and bicyclists using an agent space model Type de document : Article/Communication Auteurs : Mingwei Liu, Auteur ; Tinggui Chen, Auteur ; Chiaki Matunaga, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101725 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] agent (intelligence artificielle)
[Termes IGN] bicyclette
[Termes IGN] comportement
[Termes IGN] cycliste
[Termes IGN] direction
[Termes IGN] interaction spatiale
[Termes IGN] modèle de dispersion
[Termes IGN] modèle orienté agent
[Termes IGN] navigation pédestre
[Termes IGN] piéton
[Termes IGN] sécurité
[Termes IGN] vitesse
[Termes IGN] zone urbaineRésumé : (auteur) As the number of bicyclists in urban areas continues to increase, the need to realistically model the movement and interactions of bicyclists in mixed urban traffic is rapidly gaining importance. Therefore, this paper presents an agent space model (ASM) to elucidate the movements of bicyclists and pedestrians on shared roads. The ASM model, via simulation, particularly illustrates the dispersion phenomenon observed for non-motorized road users. The mutual interactions and diverse bicyclist and pedestrian properties were also incorporated into this model. The mutual interactions were realised through agent spaces of different sizes in conflict and overtaking behaviours for the following combinations: bicyclist-to-pedestrian, bicyclist-to-bicyclist, pedestrian-to-bicyclist, and pedestrian-to-pedestrian, which were obtained through experiments. The hypothesis test indicated that different agent spaces exist for different types of interactions. The experimental data were used to obtain several variables that describe the elements of road user agent spaces, including longitudinal and lateral distances and the dynamic relationship between the longitudinal distance and speed. The simulation results indicated that with an increase in the number of pedestrians, the maximum capacity decreased and the dispersion degree increased. The following psychological and physiological factors affect the degree of dispersion of bicyclists: travelling speed, reaction time, intensity, probability of selecting the head-on direction, and probability of selecting the right-hand direction. In addition, lane formation was observed in all simulations. The results also demonstrated that dedicated bicycle lanes will significantly reduce the dispersion degree. Moreover, the safety and efficiency effects of different forms of bicycle lanes were analysed from the perspective of the degree of dispersion. The simulation results can provide specific guidelines for understanding the causes of phenomena such as dispersion and lane formation, as well as for studying the traffic dynamics, effects of dedicated bicycle lanes, and macroscopic characteristics according to different bicyclist-pedestrian ratios. Numéro de notice : A2021-826 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101725 Date de publication en ligne : 20/10/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101725 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98947
in Computers, Environment and Urban Systems > vol 91 (January 2022) . - n° 101725[article]
Titre : A web GIS to generate audio-tactile maps for visually impaired people Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur ; Samuel Braikeh, Auteur ; Ridley Campbell, Auteur ; Jean-Marie Favreau, Auteur ; Jérémy Kalsron, Auteur Editeur : International Cartographic Association ICA - Association cartographique internationale ACI Année de publication : 2022 Collection : Abstracts of the ICA num. 5 Projets : ACTIVmap / Favreau, Jean-Marie Conférence : EuroCarto 2022, European Cartographic Conference 19/09/2022 21/09/2022 Vienne Autriche OA Proceedings Langues : Anglais (eng) Descripteur : [Termes IGN] carte tactile
[Termes IGN] personne malvoyante
[Termes IGN] WebSIG
[Vedettes matières IGN] GénéralisationNuméro de notice : C2022-038 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/ica-abs-5-115-2022 Date de publication en ligne : 14/09/2022 En ligne : http://dx.doi.org/10.5194/ica-abs-5-115-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101621 Searching for an optimal hexagonal shaped enumeration unit size for effective spatial pattern recognition in choropleth maps / Izabela Karsznia in ISPRS International journal of geo-information, vol 10 n° 9 (September 2021)PermalinkPredicting user activity intensity using geographic interactions based on social media check-in data / Jing Li in ISPRS International journal of geo-information, vol 10 n° 8 (August 2021)PermalinkComNet: combinational neural network for object detection in UAV-borne thermal images / Minglei Li in IEEE Transactions on geoscience and remote sensing, vol 59 n° 8 (August 2021)PermalinkImproving human mobility identification with trajectory augmentation / Fan Zhou in Geoinformatica, vol 25 n° 3 (July 2021)PermalinkSpatial knowledge acquisition with virtual semantic landmarks in mixed reality-based indoor navigation / Bing Liu in Cartography and Geographic Information Science, vol 48 n° 4 (July 2021)PermalinkTowards generating network of bikeways from Mapillary data / Xuan Ding in Computers, Environment and Urban Systems, vol 88 (July 2021)PermalinkImaging the subsurface: How different visualizations of cross-sections affect the sense of uncertainty / Ane Bang-Kittilsen in Journal of Geovisualization and Spatial Analysis, vol 5 n° 1 (June 2021)PermalinkEmotional cartography as a window into children's well-being: Visualizing the felt geographies of place / Andrew Steger in Emotion, Space and Society, vol 39 (May 2021)PermalinkEvaluating PPGIS usability in a multi-national field study combining qualitative surveys and eye-tracking / Mona Bartling in Cartographic journal (the), vol 58 n° 2 (May 2021)PermalinkA BiLSTM-CNN model for predicting users’ next locations based on geotagged social media / Yi Bao in International journal of geographical information science IJGIS, vol 35 n° 4 (April 2021)Permalink