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Histograms of oriented mosaic gradients for snapshot spectral image description / Lulu Chen in ISPRS Journal of photogrammetry and remote sensing, vol 183 (January 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 183 (January 2022) . - pp 79 - 93 Titre : Histograms of oriented mosaic gradients for snapshot spectral image description Type de document : Article/Communication Auteurs : Lulu Chen, Auteur ; Yong-Qiang Zhao, Auteur ; Jonathan Cheung-Wai Chan, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 79 - 93 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] capteur multibande [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection d'objet [Termes IGN] extraction de traits caractéristiques [Termes IGN] filtre spectral [Termes IGN] histogramme [Termes IGN] image proche infrarouge [Termes IGN] image spectrale [Termes IGN] mosaïque d'images [Termes IGN] poursuite de cible [Termes IGN] temps instantané Résumé : (auteur) This paper presents a feature descriptor using Histogram of Oriented Mosaic Gradient (HOMG) that extracts spatial-spectral features directly from mosaic spectral images. Spectral imaging utilizes unique spectral signatures to distinguish objects of interest in the scene more discriminatively. Snapshot spectral cameras equipped with spectral filter arrays (SFAs) capture spectral videos in real time, making it possible to detect/track fast moving targets based on spectral imaging. How to effectively extract the spatial-spectral feature directly from the mosaic spectral images acquired by snapshot spectral cameras is a core issue for detection/tracking. So far, there is a lack of comprehensive and in-depth research on this issue. To this end, this paper proposed a new spatial-spectral feature extractor for mosaic spectral images. The proposed scheme finds two forms of SFA neighborhood (SFAN) to construct a feature extractor suitable for any SFA structure. Exploiting the spatial-spectral correlation in two SFANs, we design six mosaic spatial-spectral gradient operators to compute spatial-spectral gradient maps (SGMs). HOMG descriptors are constructed using the magnitude and orientation of SGMs. The effectiveness and generalizability of the proposed method have been verified with object tracking experiments. Compared to the state-of-the-art feature descriptors, HOMG ranked first on two datasets captured with snapshot spectral camera with different SFAs, achieving a gain of 3.9% and 5.9% in average success rate over the second-ranked feature. Numéro de notice : A2022-010 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.10.018 Date de publication en ligne : 12/11/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.10.018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99058 [article]

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Multiple convolutional features in Siamese networks for object tracking / Zhenxi Li in Machine Vision and Applications, vol 32 n° 3 (May 2021)  

Public [article]  inMachine Vision and Applications > vol 32 n° 3 (May 2021) . - n° 59 Titre : Multiple convolutional features in Siamese networks for object tracking Type de document : Article/Communication Auteurs : Zhenxi Li, Auteur ; Guillaume-Alexandre Bilodeau, Auteur ; Wassim Bouachir, Auteur Année de publication : 2021 Article en page(s) : n° 59 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] approche hiérarchique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] extraction de traits caractéristiques [Termes IGN] poursuite de cible [Termes IGN] reconnaissance d'objets [Termes IGN] réseau neuronal siamois Résumé : (auteur) Siamese trackers demonstrated high performance in object tracking due to their balance between accuracy and speed. Unlike classification-based CNNs, deep similarity networks are specifically designed to address the image similarity problem and thus are inherently more appropriate for the tracking task. However, Siamese trackers mainly use the last convolutional layers for similarity analysis and target search, which restricts their performance. In this paper, we argue that using a single convolutional layer as feature representation is not an optimal choice in a deep similarity framework. We present a Multiple Features-Siamese Tracker (MFST), a novel tracking algorithm exploiting several hierarchical feature maps for robust tracking. Since convolutional layers provide several abstraction levels in characterizing an object, fusing hierarchical features allows to obtain a richer and more efficient representation of the target. Moreover, we handle the target appearance variations by calibrating the deep features extracted from two different CNN models. Based on this advanced feature representation, our method achieves high tracking accuracy, while outperforming the standard siamese tracker on object tracking benchmarks. The source code and trained models are available at https://github.com/zhenxili96/MFST. Numéro de notice : A2021-470 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00138-021-01185-7 Date de publication en ligne : 11/03/2021 En ligne : https://doi.org/10.1007/s00138-021-01185-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97903 [article]

Dynamic scene understanding using deep neural networks / Ye Lyu (2021)  

Public Titre : Dynamic scene understanding using deep neural networks Type de document : Thèse/HDR Auteurs : Ye Lyu, Auteur ; M. George Vosselman, Directeur de thèse ; Michael Ying Yang, Directeur de thèse Editeur : Enschede [Pays-Bas] : International Institute for Geo-Information Science and Earth Observation ITC Année de publication : 2021 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] attention (apprentissage automatique) [Termes IGN] chaîne de traitement [Termes IGN] champ aléatoire conditionnel [Termes IGN] compréhension de l'image [Termes IGN] détection d'objet [Termes IGN] image captée par drone [Termes IGN] image vidéo [Termes IGN] poursuite de cible [Termes IGN] régression [Termes IGN] segmentation sémantique Résumé : (auteur) Scene understanding is an important and fundamental research field in computer vision, which is quite useful for many applications in photogrammetry and remote sensing. It focuses on locating and classifying objects in images, understanding the relationships between them. The higher goal is to interpret what event happens in the scene, when it happens and why it happens, and what should we do based on the information. Dynamic scene understanding is to use information from different time to interpret scenes and answer the above related questions. For modern scene understanding technology, deep learning has shown great potential for such task. "Deep" in deep learning refers to the use of multiple layers in the neural networks. Deep neural networks are powerful as they are highly non-linear function that possess the ability to map from one domain to another quite different domain after proper training. It is the best solution for many fundamental research tasks regarding scene understanding. This ph.D. research also takes advantage of deep learning for dynamic scene understanding. Temporal information plays an important role for dynamic scene understanding. Compared with static scene understanding from images, information distilled from the time dimension provides values in many different ways. Images across consecutive frames have very high correlation, i.e., objects observed in one frame have very high chance to be observed and identified in nearby frames as well. Such redundancy in observation could potentially reduce the uncertainty for object recognition with deep learning based methods, resulting in more consistent inference. High correlation across frames could also improve the chance for recognizing objects correctly. If the camera or the object moves, the object could be observed in multiple different views with different poses and appearance. The information captured for object recognition would be more diverse and complementary, which could be aggregated to jointly inference the categories and the properties of objects. This ph.D. research involves several tasks related to the dynamic scene understanding in computer vision, including semantic segmentation for aerial platform images (chapter 2, 3), video object segmentation and video object detection for common objects in natural scenes (chapter 4, 5), and multi-object tracking and segmentation for cars and pedestrians in driving scenes (chapter 6). Chapter2 investigates how to establish the semantic segmentation benchmark for the UAV images, which includes data collection, data labeling, dataset construction, and performance evaluation with baseline deep neural networks and the proposed multi-scale dilation net. Conditional random field with feature space optimization is used to achieve consistent semantic segmentation prediction in videos. Chapter3 investigates how to better extract the scene context information for etter object recognition performance by proposing the novel bidirectional multiscale attention networks. It achieves better performance by inferring features and attention weights for feature fusing from both higher level and lower level branches. Chapter4 investigates how to simultaneously segment multiple objects across multiple frames by combining memory modules with instance segmentation networks. Our method learns to propagate the target object labels without auxiliary data, such as optical flow, which simplifies the model. Chapter5 investigates how to improve the performance of well-trained object detectors with a light weighted and efficient plug&play tracker for object detection in video. This chapter also investigates how the proposed model performs when lacking video training data. Chapter6 investigates how to improve the performance of detection, segmentation, and tracking by jointly considering top-down and bottom-up inference. The whole pipeline follows the multi-task design, i.e., a single feature extraction backbone with multiple heads for different sub-tasks. Overall, this manuscript has delved into several different computer vision tasks, which share fundamental research problems, including detection, segmentation, and tracking. Based on the research experiments and knowledge from literature review, several reflections regarding dynamic scene understanding have been discussed: The range of object context influence the quality for object recognition; The quality of video data affect the method choice for specific computer vision task; Detection and tracking are complementary for each other. For future work, unified dynamic scene understanding task could be a trend, and transformer plus self-supervised learning is one promising research direction. Real-time processing for dynamic scene understanding requires further researches in order to put the methods into usage for real-world applications. Numéro de notice : 12984 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse étrangère Note de thèse : PhD thesis : Geo-Information Science and Earth Observation : Enschede, university of Twente : 2021 DOI : 10.3990/1.9789036552233 Date de publication en ligne : 08/09/2021 En ligne : https://library.itc.utwente.nl/papers_2021/phd/lyu.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100962

A scanning LiDAR for long range detection and tracking of UAVs / Alain Quentel (2021)  

Public Titre : A scanning LiDAR for long range detection and tracking of UAVs Type de document : Thèse/HDR Auteurs : Alain Quentel, Auteur ; Yohan Dupuis, Directeur de thèse Editeur : Rouen : Université de Rouen Année de publication : 2021 Importance : 159 p. Format : 21 x 30 cm Note générale : bibliographie Thèse pour obtenir le diplôme de Doctorat, spécialité Electronique, microélectronique, optique et lasers, optoélectronique microondes robotique Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] drone [Termes IGN] optimisation (mathématiques) [Termes IGN] poursuite de cible [Termes IGN] réflectivité [Termes IGN] télémètre laser aéroporté [Termes IGN] télémétrie laser aéroporté [Termes IGN] temps de vol Index. décimale : THESE Thèses et HDR Résumé : (auteur) Misuse of civil drones, or UAVs (unmanned aerial vehicles) has been a rising concern in the past few years. As a response, multiple systems including optics, electronics and even acoustics technologies have been developed for detection and tracking. Unfortunately, UAVs represent a challenging target to detect and track due to their small, decimetric size and large variability of shapes and behaviors. In this PhD, we developed and optimized a LiDAR (light detection and ranging) system to tackle this issue to distances up to a kilometer. In our system, range is acquired using the time of flight principle, and imagery done by sequentially scanning the scene with a dual-axis galvanometer. We took advantage of the scanning versatility to develop several operating modes. A standard detection mode captures the image of the scene using a raster-scan of large field of view. Tracking mode is based on a local pattern surrounding the target, which is updated at a very high rate to keep the target within its boundaries. Efforts were put into a theoretical and numerical optimization study of the numerous parameters involved in our scanning LiDAR, so as to reach sufficient performances in term of maximal range, localization resolution and rate. Pattern optimization for both detection and tracking mode was a primary focus, using the target probability of detection as the function to maximize. Target size, speed and reflectivity was also introduced in the probability of detection, giving a complete overview of the system performance. On our LiDAR platform, developed from the ground up, each component was characterized to enrich and validate our models. This prototype was tested for UAVs detection and tracking during several weeks of trials. Following this success, a pre-industrial integration process was launched and supervised by the candidate. Numéro de notice : 28535 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de doctorat : Electronique, microélectronique, optique et lasers, optoélectronique microondes robotique : Rouen : 2021 Organisme de stage : Institut de Recherche en Systèmes Electroniques Embarqués DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-03228683 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99312

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 IGN] bande spectrale [Termes IGN] filtrage bayésien [Termes IGN] filtre de Kalman [Termes IGN] inférence statistique [Termes IGN] largeur de bande [Termes IGN] phase [Termes IGN] poursuite de cible [Termes IGN] seuillage d'image [Termes 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]

Sensor tasking for search and catalog maintenance of geosynchronous space objects / Han Cai in Acta Astronautica, vol 175 (October 2020)  

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Visual object tracking with deep neural networks / Pier Luigi Mazzeo (2019)  

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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)  

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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)  

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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)  

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

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Tracking 3D moving objects based on GPS/IMU navigation solution, laser scanner point cloud and GIS data / Siavash Hosseinyalamdary in ISPRS International journal of geo-information, vol 4 n°3 (September 2015)  

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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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