Global ETD Search

31	Efficient multiple hypothesis tracking using a purely functional array language Nolkrantz, Marcus January 2022 (has links) An autonomous vehicle is a complex system that requires a good perception of the surrounding environment to operate safely. One part of that is multiple object tracking, which is an essential component in camera-based perception whose responsibility is to estimate object motion from a sequence of images. This requires an association problem to be solved where newly estimated object positions are mapped to previously predicted trajectories, for which different solution strategies exist. In this work, a multiple hypothesis tracking algorithm is implemented. The purpose is to demonstrate that measurement associations are improved compared to less compute-intensive alternatives. It was shown that the implemented algorithm performed 13 percent better than an intersection over union tracker when evaluated using a standard evaluation metric. Furthermore, this work also investigates the usage of abstraction layers to accelerate time-critical parallel operations on the GPU. It was found that the execution time of the tracking algorithm could be reduced by 42 percent by replacing four functions with implementations written in the purely functional array language Futhark. Finally, it was shown that a GPU code abstraction layer can reduce the knowledge barrier required to write efficient CUDA kernels. multiple object tracking multiple hypothesis tracking tracking-by-detection GPGPU GPU code abstraction functional programming Futhark Computer and Information Sciences Data- och informationsvetenskap
32	Cognitive training optimization with a closed-loop system Roy, Yannick 08 1900 (has links) Les interfaces cerveau-machine (ICMs) nous offrent un moyen de fermer la boucle entre notre cerveau et le monde de la technologie numérique. Cela ouvre la porte à une pléthore de nouvelles applications où nous utilisons directement le cerveau comme entrée. S’il est facile de voir le potentiel, il est moins facile de trouver la bonne application avec les bons corrélats neuronaux pour construire un tel système en boucle fermée. Ici, nous explorons une tâche de suivi d’objets multiples en 3D, dans un contexte d’entraînement cognitif (3D-MOT). Notre capacité à suivre plusieurs objets dans un environnement dynamique nous permet d’effectuer des tâches quotidiennes telles que conduire, pratiquer des sports d’équipe et marcher dans un centre commercial achalandé. Malgré plus de trois décennies de littérature sur les tâches MOT, les mécanismes neuronaux sous- jacents restent mal compris. Ici, nous avons examiné les corrélats neuronaux via l’électroencéphalographie (EEG) et leurs changements au cours des trois phases d’une tâche de 3D-MOT, à savoir l’identification, le suivi et le rappel. Nous avons observé ce qui semble être un transfert entre l’attention et la de mémoire de travail lors du passage entre le suivi et le rappel. Nos résultats ont révélé une forte inhibition des fréquences delta et thêta de la région frontale lors du suivi, suivie d’une forte (ré)activation de ces mêmes fréquences lors du rappel. Nos résultats ont également montré une activité de retard contralatérale (CDA en anglais), une activité négative soutenue dans l’hémisphère contralatérale aux positions des éléments visuels à suivre. Afin de déterminer si le CDA est un corrélat neuronal robuste pour les tâches de mémoire de travail visuelle, nous avons reproduit huit études liées au CDA avec un ensemble de données EEG accessible au public. Nous avons utilisé les données EEG brutes de ces huit études et les avons analysées avec le même pipeline de base pour extraire le CDA. Nous avons pu reproduire les résultats de chaque étude et montrer qu’avec un pipeline automatisé de base, nous pouvons extraire le CDA. Récemment, l’apprentissage profond (deep learning / DL en anglais) s’est révélé très prometteur pour aider à donner un sens aux signaux EEG en raison de sa capacité à apprendre de bonnes représentations à partir des données brutes. La question à savoir si l’apprentissage profond présente vraiment un avantage par rapport aux approches plus traditionnelles reste une question ouverte. Afin de répondre à cette question, nous avons examiné 154 articles appliquant le DL à l’EEG, publiés entre janvier 2010 et juillet 2018, et couvrant différents domaines d’application tels que l’épilepsie, le sommeil, les interfaces cerveau-machine et la surveillance cognitive et affective. Enfin, nous explorons la possibilité de fermer la boucle et de créer un ICM passif avec une tâche 3D-MOT. Nous classifions l’activité EEG pour prédire si une telle activité se produit pendant la phase de suivi ou de rappel de la tâche 3D-MOT. Nous avons également formé un classificateur pour les essais latéralisés afin de prédire si les cibles étaient présentées dans l’hémichamp gauche ou droit en utilisant l’activité EEG. Pour la classification de phase entre le suivi et le rappel, nous avons obtenu un 80% lors de l’entraînement d’un SVM sur plusieurs sujets en utilisant la puissance des bandes de fréquences thêta et delta des électrodes frontales. / Brain-computer interfaces (BCIs) offer us a way to close the loop between our brain and the digital world of technology. It opens the door for a plethora of new applications where we use the brain directly as an input. While it is easy to see the disruptive potential, it is less so easy to find the right application with the right neural correlates to build such closed-loop system. Here we explore closing the loop during a cognitive training 3D multiple object tracking task (3D-MOT). Our ability to track multiple objects in a dynamic environment enables us to perform everyday tasks such as driving, playing team sports, and walking in a crowded mall. Despite more than three decades of literature on MOT tasks, the underlying and intertwined neural mechanisms remain poorly understood. Here we looked at the electroencephalography (EEG) neural correlates and their changes across the three phases of a 3D-MOT task, namely identification, tracking and recall. We observed what seems to be a handoff between focused attention and working memory processes when going from tracking to recall. Our findings revealed a strong inhibition in delta and theta frequencies from the frontal region during tracking, followed by a strong (re)activation of these same frequencies during recall. Our results also showed contralateral delay activity (CDA), a sustained negativity over the hemisphere contralateral to the positions of visual items to be remembered. In order to investigate if the CDA is a robust neural correlate for visual working memory (VWM) tasks, we reproduced eight CDA-related studies with a publicly accessible EEG dataset. We used the raw EEG data from these eight studies and analysed all of them with the same basic pipeline to extract CDA. We were able to reproduce the results from all the studies and show that with a basic automated EEG pipeline we can extract a clear CDA signal. Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representations from raw data. Whether DL truly presents advantages as compared to more traditional EEG processing approaches, however, remains an open question. In order to address such question, we reviewed 154 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, brain-computer interfacing, and cognitive and affective monitoring. Finally, we explore the potential for closing the loop and creating a passive BCI with a 3D-MOT task. We classify EEG activity to predict if such activity is happening during the tracking or the recall phase of the 3D-MOT task. We also trained a classifier for lateralized trials to predict if the targets were presented on the left or right hemifield using EEG brain activity. For the phase classification between tracking and recall, we obtained 80% accuracy when training a SVM across subjects using the theta and delta frequency band power from the frontal electrodes and 83% accuracy when training within subjects. Multiple-Object Tracking MOT Contralateral Delay Activity CDA EEG Working Memory Attention Deep Learning Suivi d’objets multiples Mémoire de travail Apprentissage profond
33	Unsupervised multiple object tracking on video with no ego motion / Oövervakad spårning av flera objekt på video utan egorörelse Wu, Shuai January 2022 (has links) Multiple-object tracking is a task within the field of computer vision. As the name stated, the task consists of tracking multiple objects in the video, an algorithm that completes such task are called trackers. Many of the existing trackers require supervision, meaning that the location and identity of each object which appears in the training data must be labeled. The procedure of generating these labels, usually through manual annotation of video material, is highly resource-consuming. On the other hand, different from well-known labeled Multiple-object tracking datasets, there exist a massive amount of unlabeled video with different objects, environments, and video specifications. Using such unlabeled video can therefore contribute to cheaper and more diverse datasets. There have been numerous attempts on unsupervised object tracking, but most rely on evaluating the tracker performance on a labeled dataset. The reason behind this is the lack of an evaluation method for unlabeled datasets. This project explores unsupervised pedestrian tracking on video taken from a stationary camera over a long duration. On top of a simple baseline tracker, two methods are proposed to extend the baseline to increase its performance. We then propose an evaluation method that works for unlabeled video, which we use to evaluate the proposed methods. The evaluation method consists of the trajectory completion rate and the number of ID switches. The trajectory completion rate is a novel metric proposed for pedestrian tracking. Pedestrians generally enter and exit the scene for video taken by a stationary camera in specific locations. We define a complete trajectory as a trajectory that goes from one area to another. The completion rate is calculated by the number of complete trajectories over all trajectories. Results showed that the two proposed methods had increased the trajectory completion rate on top of the original baseline performance. Moreover, both proposed methods did so without significantly increasing the number of ID switches. / Spårning av flera objekt är en uppgift inom området datorseende. Som namnet angav består uppgiften av att spåra flera objekt i videon, en algoritm som slutför en sådan uppgift kallas trackers. Många av de befintliga spårarna kräver övervakning, vilket innebär att platsen och identiteten för varje objekt som visas i träningsdata måste märkas. Proceduren för att generera dessa etiketter, vanligtvis genom manuell anteckning av videomaterial, är mycket resurskrävande. Å andra sidan, till skillnad från välkända märkta uppsättningar för spårning av flera objekt, finns det en enorm mängd omärkt video med olika objekt, miljöer och videospecifikationer. Att använda sådan omärkt video kan därför bidra till billigare och mer varierande datauppsättningar. Det har gjorts många försök med oövervakad objektspårning, men de flesta förlitar sig på att utvärdera spårningsprestandan på en märkt dataset. Anledningen till detta är avsaknaden av en utvärderingsmetod för omärkta datamängder. Detta projekt utforskar oövervakad fotgängarspårning på video som tagits från en stillastående kamera under lång tid. Utöver en enkel baslinjespårare föreslås två metoder för att utöka baslinjen för att öka dess prestanda. Vi föreslår sedan en utvärderingsmetod som fungerar för omärkt video, som vi använder för att utvärdera de föreslagna metoderna. Utvärderingsmetoden består av banans slutförandegrad och antalet ID-växlar. Banans slutförandegrad är ett nytt mått som föreslås för spårning av fotgängare. Fotgängare går vanligtvis in och lämnar scenen för video tagna med en stillastående kamera på specifika platser. Vi definierar en komplett bana som en bana som går från ett område till ett annat. Färdigställandegraden beräknas av antalet kompletta banor över alla banor. Resultaten visade att de två föreslagna metoderna hade ökat graden av fullbordande av banan utöver den ursprungliga baslinjeprestandan. Dessutom gjorde båda de föreslagna metoderna det utan att nämnvärt öka antalet ID-växlar. Object tracking Multiple-object tracking Unsupervised learning Evaluation metric Pedestrian tracking Objektspårning Spårning av flera objekt Oövervakad inlärning Utvärderingsmått Fotgängarspårning Computer and Information Sciences Data- och informationsvetenskap
34	Pedestrian Multiple Object Tracking in Real-Time / Spårning av flera fotgängare i realtid Wintzell, Samuel January 2022 (has links) Multiple object tracking (MOT) is the task of detecting multiple objects in a scene and associating detections over time to form tracks. It is essential for many scene understanding tasks like surveillance, robotics and autonomous driving. Nowadays, the dominating tracking pipeline is to first detect all individual objects in a scene followed by a separate data association step, also known as tracking-by-detection. Recently, methods doing simultaneous detection and tracking has emerged, combining the task of detection and tracking into one single framework. In this project, we analyse performance of multiple object tracking algorithms belonging to both tracking categories. The goal is to examine strengths, weaknesses, and real-time capability of different tracking approaches in order to understand their suitability in different applications. Results show that a tracking-by-detection system with Scaled-YOLOv4 and SORT achieves 46.8% accuracy at over 28 frames per second (FPS) on Nvidia GTX 1080. By reducing the input resolution, inference speed is increased to almost 50 FPS, making it well suitable for real-time application. The addition of a deep re-identification CNN reduces the number of identity switches by 47%. However, association speed drops as low as 14 FPS for densely populated scenes. This indicates that re-identification CNNs may be impractical for safety critical applications like autonomous driving, especially in urban environments. Simultaneous detection and tracking results suggests an increased tracking robustness. The removal of a complex data association strategy improves robustness with respect to extended modules like re-identification. This indicates that the inherent simplicity in the simultaneous detection and tracking paradigm can provide robust baseline trackers for a variety of applications. We note that further research is required to strengthen this notion. / Multipel objektspårning handlar om att detektera alla objekt i bilder och associera dem över tid för att bilda spår. Det är ett viktigt ämne inom datorseende för flera applikationer, däribland kameraövervakning, robotik och självkörande fordon. Idag är det dominerande tillvägagångsättet inom objektspårning att först detektera alla objekt och sedan associera dem i ett separat steg, också kallat spårning-genom-detektion. På senare tid har det framkommit nya metoder som detekterar och spårar samtidigt. I detta projekt analyserar vi prestanda av metoder som tillämpar båda tillvägagångssätt. Målet med projektet är att undersöka styrkor, svagheter och hur väl metoderna lämpar sig för att användas i realtid. Detta för att förstå hur olika objektspårare kan anpassas till olika praktiska applikationer. Resultaten visar att ett system som tillämpar spårning-genom-detektion med Scaled-YOLOv4 och SORT, uppnår 46.8% noggrannhet med en hastighet på över 28 bildrutor per sekund. Detta på en Nvidia GTX 1080. Genom att minska bildupplösningen når hastigheten nästan hela vägen upp till 50 bildrutor per sekund, vilket gör systemet väl lämpat för realtidsapplikation. Genom att addera ett djupt nätverk för återidentifiering minskar antalet identitetsbyten med 47%. Samtidigt minskar också hastigheten för spårning till 14 bildrutor per sekund i välbefolkade miljöer. Detta indikerar att djupa nätverk för återidentifiering inte lämpar sig för säkerhetskritiska applikationer såsom självkörande fordon. Särskilt i urbana miljöer. Resultat för system som detekterar och spårar samtidigt antyder att de är mer robusta. Genom att ta bort komplexa strategier för associering blir systemen robusta mot ytterligare moduler såsom återidentifiering. Det ger en indikation på att den inneboende enkelheten i dessa system resulterar i objektspårare som kan fungera som grunder i många olika applikationer. Vi noterar att ytterligare forsking behövs för att styrka denna idé. Computer Vision Deep Learning Multiple Object Tracking Object Detection Person Re-identification Datorseende Djupinlärning Spårning av Flera Objekt Objektdetektering Personidentifiering Elektroteknik och elektronik
35	Dim Object Tracking in Cluttered Image Sequences Ahmadi, Kaveh, ahmadi January 2016 (has links) No description available. Computer Engineering Computer Science Dim Object Tracking Particle Filter Particle Swarm Optimization Multiple Object Tracking Multi Sensor Object Tracking Super Resolution
36	Efficient hierarchical layered graph approach for multi-region segmentation / Abordagem eficiente baseada em grafo hierárquico em camadas para a segmentação de múltiplas regiões Leon, Leissi Margarita Castaneda 15 March 2019 (has links) Image segmentation refers to the process of partitioning an image into meaningful regions of interest (objects) by assigning distinct labels to their composing pixels. Images are usually composed of multiple objects with distinctive features, thus requiring distinct high-level priors for their appropriate modeling. In order to obtain a good segmentation result, the segmentation method must attend all the individual priors of each object, as well as capture their inclusion/exclusion relations. However, many existing classical approaches do not include any form of structural information together with different high-level priors for each object into a single energy optimization. Consequently, they may be inappropriate in this context. We propose a novel efficient seed-based method for the multiple object segmentation of images based on graphs, named Hierarchical Layered Oriented Image Foresting Transform (HLOIFT). It uses a tree of the relations between the image objects, being each object represented by a node. Each tree node may contain different individual high-level priors and defines a weighted digraph, named as layer. The layer graphs are then integrated into a hierarchical graph, considering the hierarchical relations of inclusion and exclusion. A single energy optimization is performed in the hierarchical layered weighted digraph leading to globally optimal results satisfying all the high-level priors. The experimental evaluations of HLOIFT and its extensions, on medical, natural and synthetic images, indicate promising results comparable to the state-of-the-art methods, but with lower computational complexity. Compared to hierarchical segmentation by the min cut/max-flow algorithm, our approach is less restrictive, leading to globally optimal results in more general scenarios, and has a better running time. / A segmentação de imagem refere-se ao processo de particionar uma imagem em regiões significativas de interesse (objetos), atribuindo rótulos distintos aos seus pixels de composição. As imagens geralmente são compostas de vários objetos com características distintas, exigindo, assim, restrições de alto nível distintas para a sua modelagem apropriada. Para obter um bom resultado de segmentação, o método de segmentação deve atender a todas as restrições individuais de cada objeto, bem como capturar suas relações de inclusão/ exclusão. No entanto, muitas abordagens clássicas existentes não incluem nenhuma forma de informação estrutural, juntamente com diferentes restrições de alto nível para cada objeto em uma única otimização de energia. Consequentemente, elas podem ser inapropriadas nesse contexto. Estamos propondo um novo método eficiente baseado em sementes para a segmentação de múltiplos objetos em imagens baseado em grafos, chamado Hierarchical Layered Oriented Image Foresting Transform (HLOIFT). Ele usa uma árvore das relações entre os objetos de imagem, sendo cada objeto representado por um nó. Cada nó da árvore pode conter diferentes restrições individuais de alto nível, que são usadas para definir um dígrafo ponderado, nomeado como camada. Os grafos das camadas são então integrados em um grafo hierárquico, considerando as relações hierárquicas de inclusão e exclusão. Uma otimização de energia única é realizada no dígrafo hierárquico em camadas, levando a resultados globalmente ótimos, satisfazendo todas as restrições de alto nível. As avaliações experimentais do HLOIFT e de suas extensões, em imagens médicas, naturais e sintéticas,indicam resultados promissores comparáveis aos métodos do estado-da-arte, mas com menor complexidade computacional. Comparada à segmentação hierárquica pelo algoritmo min-cut/max-flow, nossa abordagem é menos restritiva, levando a resultados globalmente ótimo sem cenários mais gerais e com melhor tempo de execução. Hierarchical image segmentation Image segmentation based on graphs Interactive segmentation Medical image segmentation Multiple object segmentation Segmentação de imagens médicas Segmentação de múltiplos objetos Segmentação hierárquica de imagens Segmentação interativa Superpixels Superpixels
37	Suivi visuel d'objets dans un réseau de caméras intelligentes : application au systèmes de manutention automatisés / Multiple object tracking on smart cameras : application to automated handling systems Benamara, Mohamed Adel 19 December 2018 (has links) L’intralogistique (ou logistique interne) s’intéresse au traitement et à l’optimisation des flux physiques au sein des entrepôts, centres de distribution et usines. Les systèmes de manutention automatisés sont au cœur de la logistique interne de plusieurs industries comme le commerce en ligne, la messagerie postale, la grande distribution, l’industrie manufacturière, le transport aéroportuaire, etc. Ces équipements composés de lignes de convoyage haute cadence permettent un transport sûr et fiable d’un volume considérable de biens et de marchandises tout en réduisant les coûts.L’automatisation de l’acheminement des flux physiques par les systèmes de manutention repose sur l’identification et le suivi en temps réel des charges transportées. Dans cette thèse, nous explorons une solution de suivi qui emploie un réseau de caméras intelligentes à champs recouvrants. L’objectif final étant de fournir l’information de suivi sur les charges transportées pour le pilotage d’un système de manutention.Le suivi d’objets est un problème fondamental de la vision par ordinateur qui a de nombreuses applications comme la vidéosurveillance, la robotique, les voitures autonomes, etc. Nous avons intégré plusieurs briques de base issues de la vidéosurveillance et traditionnellement appliquées aux scènes de surveillance automobile ou de surveillance des activités humaines pour constituer une chaine de suivi de référence. Cette chaine d’analyse vidéo étalon nous a permis de caractériser des hypothèses propres au convoyage d’objet. Nous proposons dans cette thèse d’incorporer cette connaissance métier dans la chaine de suivi pour en améliorer les performances. Nous avons, notamment pris en compte, dans l’étape de segmentation des images, le fait que les objets doivent pouvoir s’arrêter sans pour autant être intégrés aux modèles d’arrière-plan. Nous avons également exploité la régularité des trajectoires des objets convoyés dans les installations, permettant d’améliorer les modèles prédictifs de la position et de la vitesse des objets, dans les étapes de suivi. Enfin, nous avons intégré des contraintes de stricte monotonie dans l’ordre des colis sur le convoyeur, contraintes qui n’existent pas dans les scènes généralistes, pour ré-identifier les objets dans les situations où ils sont proches des eux les autres.Nous nous sommes par ailleurs attelés à un problème pratique d’optimisation des performances sur l’architecture multi-cœurs couplée aux caméras intelligentes. Dans ce cadre, nous avons a mis en place un apprentissage dynamique de la zone de l’image contenant le convoyeur. Cette zone d’intérêt nous a permis de limiter la mise à jour du modèle de fond à cette seule zone. Nous avons, par la suite, proposé une stratégie de parallélisation qui partitionne de manière adaptative cette région d’intérêt de l’image, afin d’équilibrer au mieux la charge de travail entre les différents cœurs de l’architecture des caméras intelligentes.Nous avons également traité la problématique du suivi sur plusieurs caméras. Nous avons proposé une approche basée sur un système de composition d’évènements. Cette approche nous a permis de fusionner les données de suivi local pour former les trajectoires globales des colis, tout en intégrant des informations issues du processus métier, par exemple la saisie de l’information de destination par des opérateurs sur un terminal avant la dépose des colis. Nous avons validé cette approche sur un système de manutention mis en place dans un centre de tri postal de grande envergure. Le réseau de caméras déployé est composé de 32 caméras qui assurent le suivi de plus de 400.000 colis/jour sur des lignes de dépose. Le taux d’erreur du suivi obtenu est inférieur à 1 colis sur 1000 (0,1%). / Intralogistics (or internal logistics) focuses on the management and optimization of internal production and distribution processes within warehouses, distribution centers, and factories. Automated handling systems play a crucial role in the internal logistics of several industries such as e-commerce, postal messaging, retail, manufacturing, airport transport, etc. These systems are composed by multiple high-speed conveyor lines that provide safe and reliable transportation of a large volume of goods and merchandise while reducing costs.The automation of the conveying process relies on the identification and the real-time tracking of the transported loads. In this thesis, we designed a tracking solution that employs a network of smart cameras with an overlapping field of view. The goal is to provide tracking information to control an automated handling system.Multiple object tracking is a fundamental problem of computer vision that has many applications such as video surveillance, robotics, autonomous cars, etc. We integrated several building blocks traditionally applied to traffic surveillance or human activities monitoring to constitute a tracking pipeline. We used this baseline tracking pipeline to characterize contextual scene information proper to the conveying scenario. We integrated this contextual information to the tracking pipeline to enhance the performance. In particular, we took into account the state of moving objects that become stationary in the background subtraction step to prevent their absorption to the background model. We have also exploited the regularity of objects trajectory to enhance the motion model associated with the tracked objects. Finally, we integrated the precedence ordering constraint among the conveyed object to reidentify them when they are close to each other.We have also tackled practical problems related to the optimization the execution of the proposed tracking problem in the multi-core architectures of smart cameras. In particular, we proposed a dynamic learning process that extracts the region of the image that corresponds to the conveyor lines. We reduced the number of the processed pixel by restricting the processing to this region of interest. We also proposed a parallelization strategy that adaptively partitions this region of interest of the image, in order to balance the workload between the different cores of the smart cameras.Finally, we proposed a multiple cameras tracking algorithms based on event composition. This approach fuses the local tracking generated by the smart cameras to form global object trajectories and information from third party systems such as the destination of the object entered by operators on a terminal. We validated the proposed approach for the control of a sorting system deployed in a postal distribution warehouse. A network of cameras composed of 32 cameras tracks more than 400.000 parcel/day in injections lines. The tracking error rate is less than 1 parcel in a 1000 (0.1%). Intra-logistique Logistique interne Systèmes de manutention automatisée Suivi d’objets Soustraction de fond Caméras intelligentes Réseau de caméras Intra-logistics Internal logistics Automated handling systems Multiple object tracking Background subtraction Smart cameras Camera network 004
38	Monitorování dopravy z leteckých videí / Traffic Monitoring from Aerial Video Data Babinec, Adam January 2015 (has links) This thesis proposes a system for extraction of vehicle trajectories from aerial video data for traffic analysis. The system is designed to analyse video sequence of a single traffic scene captured by an action camera mounted on an arbitrary UAV flying at the altitudes of approximately 150 m. Each video frame is geo-registered using visual correspondence of extracted ORB features. For the detection of vehicles, MB-LBP classifier cascade is deployed, with additional step of pre-filtering of detection candidates based on movement and scene context. Multi-object tracking is achieved by Bayesian bootstrap filter with an aid of the detection algorithm. The performance of the system was evaluated on three extensively annotated datasets. The results show that on the average, 92% of all extracted trajectories are corresponding to the reality. The system is already being used in the research to aid the process of design and analysis of road infrastructures.
39	Monitorování pohybu více objektů užitím křížové korelace / Following of multiple object movement by means of cross correlation Málková, Eliška January 2019 (has links) Tato práce popisuje metodu analýzy translačního pohybu užitím křížové korelace. Ukazujeme, jakým způsobem se chová funkce křížové korelace obrazů s navzájem posunutými objekty, a jak nám to umožňuje nacházet jejich vektory posunu. Pro následnou implementaci je nalezena efektivní metoda pro hledání pouze požadovaného počtu lokálních maxim funkce.
40	Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms Vestin, Albin, Strandberg, Gustav January 2019 (has links) Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, instead of filtering to estimate the true target states. With this method, validation data for online, causal, target tracking algorithms can be obtained for all traffic scenarios without the need of extra sensors. We investigate how non-causal algorithms affects the target tracking performance using multiple sensors and dynamic models of different complexity. This is done to evaluate real-time methods against estimates obtained from non-causal filtering. Two different measurement units, a monocular camera and a LIDAR sensor, and two dynamic models are evaluated and compared using both causal and non-causal methods. The system is tested in two single object scenarios where ground truth is available and in three multi object scenarios without ground truth. Results from the two single object scenarios shows that tracking using only a monocular camera performs poorly since it is unable to measure the distance to objects. Here, a complementary LIDAR sensor improves the tracking performance significantly. The dynamic models are shown to have a small impact on the tracking performance, while the non-causal application gives a distinct improvement when tracking objects at large distances. Since the sequence can be reversed, the non-causal estimates are propagated from more certain states when the target is closer to the ego vehicle. For multiple object tracking, we find that correct associations between measurements and tracks are crucial for improving the tracking performance with non-causal algorithms. evaluation target tracking multiple sensors non-causal smoother smoothing tracking vehicle tracking camera lidar estimate estimation prediction vehicle dynamics sensor fusion real-time tracking extended kalman filter filter validation validation position estimation velocity estimation dynamic model model complexity multi object tracking multiple object tracking single object tracking data association tracking fundamentals iterated kalman filter track management gnn global nearest neighbour mahalanobis mahalanobis distance performance evaluation differential gps dgps roi ego several sensors sensors rmse root mean square error invertible motion anti-causal motion anti-causal tracking constant velocity gnn imu tfs two filter smoother ekf rts radar inertial measurement unit nonlinear nonlinear systems mono camera monocular camera noise model tracking performance fixed interval smoothing m/n logic centralized fusion non-causal object tracker car tracking car dynamics automotive active safety object tracking automotive industry thesis master reverse dynamics reverse tracking reverse sequence sequence tracking data propagation ground truth estimating ground truth additional sensors mounted sensors true estimates environment comparison algorithm independent targets overlapping measurements occluded track switch improve lower uncertainty more certain state process noise covariance sampling image sprt adas cnn cv pdf track target ego tracker tentative track observatiom online tracking offline tracking online offline recorded sequences robust self driving self-driving car traffic trajectory true state scenario scenarios future accurate output advanced driver assistance systems non-linear complex noise pedestrian truck bus maneuvering vehicles processed measurement frame state correction probability density function tuning likelihood transition measurement motion model recursion gaussian approximation distribution linear jacobian multiplicative noise ratio ad hoc ad hoc state space approach backward auction euclidean distance statistical threshold gating association margin normalize covariance matrix fusion confirmed rejected tentative history absolute error modular ego motion parameters variables logg hardware specification fused causal factorization independent uncorrelated transform moving rotation translation oncoming overtaking Control Engineering Reglerteknik

Search results