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Integrated Kalman filter of accurate ranging and tracking with wideband radar / Shaopeng Wei in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 58 n° 12 (December 2020) . - pp 8395 - 8411 Titre : Integrated Kalman filter of accurate ranging and tracking with wideband radar Type de document : Article/Communication Auteurs : Shaopeng Wei, Auteur ; Lei Zhang, Auteur ; Hongwei Liu, Auteur Année de publication : 2020 Article en page(s) : pp 8395 - 8411 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] bande spectrale [Termes descripteurs IGN] filtrage bayésien [Termes descripteurs IGN] filtre de Kalman [Termes descripteurs IGN] inférence statistique [Termes descripteurs IGN] largeur de bande [Termes descripteurs IGN] phase [Termes descripteurs IGN] poursuite de cible [Termes descripteurs IGN] seuillage d'image [Termes descripteurs IGN] signal Résumé : (auteur) Accurate ranging and wideband tracking are treated as two independent and separate processes in traditional radar systems. As a result, limited by low data rate due to nonsequential processing, accurate ranging usually performs low efficiency in practical application. Similarly, without applying accurate ranging, the data after thresholding and clustering are used in wideband tracking, leading to a significant decrease in tracking accuracy. In this article, an integrated Kalman filter of accurate ranging and tracking is proposed using methods of phase-derived-ranging and Bayesian inference in wideband radar. Besides the motion state, in this integrated Kalman filter, the complex-valued high-resolution range profile (HRRP) is also introduced as a reference signal by coherent integration in a sliding window, which incorporates target’s scattering distribution and phase characteristics. Corresponding kinetic equations are derived to predict the motion state and the reference signal in the next moment. A ranging process is constructed based on the received signal and the predicted reference signal in order to estimate innovation using methods of phase-derived-ranging and Bayesian inference, and a sequential update for motion state can be accomplished with the Kalman filter as well. In every recursion, the complex-valued reference signal is also updated by coherently integrating the latest pulses. The integrated Kalman filter takes full use of high range resolution and phase information, improving both efficiency and precision compared with conventional approaches of ranging and wideband tracking. Implemented in a sequential manner, the integrated Kalman filter can be applied in a real-time application, realizing simultaneous ranging with high precision and wideband tracking. Finally, simulated and real-measured experiments confirm the remarkable performance. Numéro de notice : A2020-740 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2987854 date de publication en ligne : 29/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2987854 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96367 [article]

Adaptive correlation filters with long-term and short-term memory for object tracking / Chao Ma in International journal of computer vision, vol 126 n° 8 (August 2018)  

Public [article]  inInternational journal of computer vision > vol 126 n° 8 (August 2018) . - pp 771 - 796 Titre : Adaptive correlation filters with long-term and short-term memory for object tracking Type de document : Article/Communication Auteurs : Chao Ma, Auteur ; Jia-Bin Huang, Auteur ; Xiaokang Yang, Auteur ; Ming-Hsuan Yang, Auteur Année de publication : 2018 Article en page(s) : pp 771 - 796 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] filtre adaptatif [Termes descripteurs IGN] méthode fondée sur le noyau [Termes descripteurs IGN] méthode robuste [Termes descripteurs IGN] poursuite de cible Résumé : (Auteur) Object tracking is challenging as target objects often undergo drastic appearance changes over time. Recently, adaptive correlation filters have been successfully applied to object tracking. However, tracking algorithms relying on highly adaptive correlation filters are prone to drift due to noisy updates. Moreover, as these algorithms do not maintain long-term memory of target appearance, they cannot recover from tracking failures caused by heavy occlusion or target disappearance in the camera view. In this paper, we propose to learn multiple adaptive correlation filters with both long-term and short-term memory of target appearance for robust object tracking. First, we learn a kernelized correlation filter with an aggressive learning rate for locating target objects precisely. We take into account the appropriate size of surrounding context and the feature representations. Second, we learn a correlation filter over a feature pyramid centered at the estimated target position for predicting scale changes. Third, we learn a complementary correlation filter with a conservative learning rate to maintain long-term memory of target appearance. We use the output responses of this long-term filter to determine if tracking failure occurs. In the case of tracking failures, we apply an incrementally learned detector to recover the target position in a sliding window fashion. Extensive experimental results on large-scale benchmark datasets demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods in terms of efficiency, accuracy, and robustness. Numéro de notice : A2018-414 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-018-1076-4 date de publication en ligne : 16/03/2018 En ligne : https://doi.org/10.1007/s11263-018-1076-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90897 [article]

Object tracking with robotic total stations : current technologies and improvements based on image data / Matthias Ehrhart in Journal of applied geodesy, vol 11 n° 3 (September 2017)  

Public [article]  inJournal of applied geodesy > vol 11 n° 3 (September 2017) . - pp 131 - 142 Titre : Object tracking with robotic total stations : current technologies and improvements based on image data Type de document : Article/Communication Auteurs : Matthias Ehrhart, Auteur ; Werner Lienhart, Auteur Année de publication : 2017 Article en page(s) : pp 131 - 142 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Topographie [Termes descripteurs IGN] instrumentation Leica [Termes descripteurs IGN] instrumentation Topcon [Termes descripteurs IGN] instrumentation Trimble [Termes descripteurs IGN] positionnement par GNSS [Termes descripteurs IGN] poursuite de cible [Termes descripteurs IGN] prisme (optique) [Termes descripteurs IGN] tachéomètre électronique robotisé [Termes descripteurs IGN] vidéotachéomètre Résumé : (Auteur) The importance of automated prism tracking is increasingly triggered by the rising automation of total station measurements in machine control, monitoring and one-person operation. In this article we summarize and explain the different techniques that are used to coarsely search a prism, to precisely aim at a prism, and to identify whether the correct prism is tracked. Along with the state-of-the-art review, we discuss and experimentally evaluate possible improvements based on the image data of an additional wide-angle camera which is available for many total stations today. In cases in which the total station’s fine aiming module loses the prism, the tracked object may still be visible to the wide-angle camera because of its larger field of view. The theodolite angles towards the target can then be derived from its image coordinates which facilitates a fast reacquisition of the prism. In experimental measurements we demonstrate that our image-based approach for the coarse target search is 4 to 10-times faster than conventional approaches. Numéro de notice : A2017-568 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1515/jag-2016-0043 En ligne : https://doi.org/10.1515/jag-2016-0043 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86688 [article]

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 descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] carrefour [Termes descripteurs IGN] compréhension de l'image [Termes descripteurs IGN] image vidéo [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] position [Termes descripteurs IGN] poursuite de cible [Termes descripteurs IGN] prévision [Termes descripteurs IGN] réalité de terrain [Termes descripteurs 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]

Probabilistic multi-person localisation and tracking / Tobias Klinger (2016) 

Public Titre : Probabilistic multi-person localisation and tracking Type de document : Thèse/HDR Auteurs : Tobias Klinger, Auteur ; Ingo Neumann, Directeur de thèse Editeur : Munich : Bayerische Akademie der Wissenschaften Année de publication : 2016 Collection : DGK - C, ISSN 0065-5325 num. 787 Importance : 125 p. Format : 21 x 30 cm ISBN/ISSN/EAN : 978-3-7696-5199-7 Note générale : bibliographie PhD Dissertation Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse de sensibilité [Termes descripteurs IGN] analyse multicritère [Termes descripteurs IGN] cible mobile [Termes descripteurs IGN] classification [Termes descripteurs IGN] détection de piéton [Termes descripteurs IGN] géolocalisation [Termes descripteurs IGN] image isolée [Termes descripteurs IGN] modèle stochastique [Termes descripteurs IGN] piéton [Termes descripteurs IGN] poursuite de cible [Termes descripteurs IGN] programmation linéaire [Termes descripteurs IGN] séquence d'images [Termes descripteurs IGN] similitude [Termes descripteurs IGN] surveillance Résumé : (auteur) This dissertation investigates the problem of localising multiple persons in image sequences, while, at the same time, establishing temporal correspondences between single-frame locations. The aim of this work is the improvement of the reliability and precision of the generated trajectories, which is addressed by the formulation and investigation of a joint probabilistic model for the recursive filtering of the estimated positions. The trajectories are estimated in a common 3D object coordinate system, which was previously almost exclusively done in 2D. Note de contenu : 1. Introduction 1.1. Motivation 1.2. Research objectives and contributions 1.3. Outline of the dissertation 2. Basics 2.1. Probabilistic modelling 2.2. Recursive Bayesian estimation 2.3. Gaussian Process Regression 3. Related work 3.1. Tracking approaches 3.2. Observations 3.3. Temporal modelling 3.4. Data association 3.5. Discussion 4. A new probabilistic approach for multi-person localisation and tracking 4.1. Problem statement via Dynamic Bayesian Network 4.2. Observations 4.3. Temporal model 4.4. data association 4.5. Recursive estimation 4.6. Discussion 5. Experiments 5.1. Datasets and evaluation criteria 5.2. Sensitivity study and training 5.3. Model validation by ablation of its components 5.4. Multi-person localisation and tracking evaluation 6. Discussion of the results 6.1. Method evaluation 6.2. Evaluation of the trajectories 7. Conclusions and future work Numéro de notice : 19793 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse étrangère Note de thèse : PhD Dissertation : : Stuttgart : 2016 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85037

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Tracking-Learning-Detection / Zdenek Kalal in IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI, vol 34 n° 7 (July 2012)

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Indoor pedestrian navigation using foot-mounted IMU and portable ultrasound range sensors / Gabriel Girard in Sensors, vol 11 n° 8 (August 2011)  

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Photogrammetric image analysis PIA 11, Munich, Germany, October 5-7, 2011 / Uwe Stilla (2011)  

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