Global ETD Search

1	Improving Image Based Fruitcount Estimates Using Multiple View-Points Stein, Madeleine January 2016 (has links) This master-thesis presents an approach to track and count the number of fruit incommercial mango orchards. The algorithm is intended to enable precision agri-culture and to facilitate labour and post-harvest storage planning. The primary objective is to develop an multi-view algorithm and investigate how it can beused to mitigate the effects of visual occlusion, to improve upon estimates frommethods that use a single central or two opposite viewpoints. Fruit are detectedin images by using two classification methods: dense pixel-wise cnn and regionbased r-cnn detection. Pair-wise fruit correspondences are established between images by using geometry provided by navigation data, and lidar data is used to generate image masks for each separate tree, to isolate fruit counts to individual trees. The tracked fruit are triangulated to locate them in 3D space, and spatial statistics are calculated over whole orchard blocks. The estimated tree counts are compared to single view estimates and validated against ground truth data of 16 mango trees from a Bundaberg mango orchard in Queensland, Australia. The results show a high R2-value of 0.99335 for four hand labelled trees and a highest R2-value of 0.9165 for the machine labelled images using the r-cnn classifier forthe 16 target trees. Computer vision epipolar geometry fruit count fruit yield mango mono camera lidar
2	Detection and tracking of overtaking vehicles / Detektion samt följning av omkörande fordon Hultqvist, Daniel January 2013 (has links) The car has become bigger, faster and more advanced for each passing year since its first appearance, and the safety requirements have also become stricter. Computer vision based support is a growing area of safety features where the car is equipped with a mono- or stereo camera. It can be used for detecting pedestrians walking out in the street, give a warning for wild-life during a cold January night using night-vision cameras and much more. This master thesis investigates the problem of detecting and tracking overtaking vehicles. Vehicles that overtake are only partly visible in the beginning, rendering it hard for standard detection/classification algorithms to get a positive detection. The need to quickly detect an incoming vehicle is crucial to be able to take fast counter-measure, such as braking, if needed. A novel approach referred to as the \textit{Wall detector} is suggested, detecting incoming vehicles using one-dimensional optical flow. Under the assumption that an overtaking car is moving in parallel to the ego-vehicle, both cars are moving towards the vanishing point in the image. A detection wall, consisting of several detection lines moving towards the vanishing point, is created, making all objects that are moving parallel to the ego-vehicle move along these lines. The result is a light-weight and fast detector with good detection performance in real-time. Several approaches for the Wall detector are implemented and evaluated, revealing that a feature based approach is the best choice. The information from the system can be used as input to heavier algorithms, boosting the confidence or to initialize a track. vehicle detection mono camera optical flow vehicle tracking vehicle overtaking vehicle cut-in
3	Localisation Absolue par Mono-caméra d'un Véhicule en Milieu Urbain via l'utilisation de Street View / Absolute Localization by Mono-camera for a Vehicle in Urban Area using Street View Yu, Li 06 April 2018 (has links) Dans un travail réalisé au Centre de Robotique et à l'Institut VEDECOM, nous nous sommes intéressés aux systèmes robustes de localisation visuelle en milieu urbain pour la voiture autonome. Obtenir une pose exacte à partir d'une mono-caméra est difficile et insuffisant en terme de précision pour la voiture autonome actuelle. Nous nous sommes concentrés sur l'utilisation de Systèmes d'Information Géographiques (SIG) pour concevoir une approche fiable, précise et absolue de localisation en milieu urbain.Le développement de SIG publics nous a apporté un nouvel horizon pour résoudre le problème de la localisation, mais ses informations, telles que les cartes topologiques, sémantiques, métriques, les Street Views, les cartes de profondeur, les cartes cadastrales 3D et les cartes en haute définition, doivent être bien analysées et organisées pour extraire les informations pertinentes pour une voiture autonome. Notre première tâche consistait à concevoir une base de données hors ligne accessible par un robot à partir d'un SIG public dense, à savoir Google Maps, qui a l'avantage d'avoir une couverture mondiale. Nous générons une représentation topométrique compacte de l'environnement urbain en extrayant quatre données utiles du SIG, y compris : les topologies, les géo-coordonnées, les Street Views panoramiques et les cartes de profondeur associées. Dans le même temps, un ensemble de données en ligne a été acquis par une mono-caméra équipée sur les véhicules de VEDECOM. Afin de rendre les Street View sphériques compatibles avec l'imagerie en ligne, une transformation basée sur l'interpolation d'image est introduite pour obtenir des images rectilignes à partir de Street Views.Nous proposons deux méthodes de localisation : l'une est une approche de vision par ordinateur basée sur l'extraction de caractéristiques, l'autre est une méthode d'apprentissage basée sur les réseaux de neurones convolutionnels (convnet). En vision par ordinateur, l'extraction de caractéristiques est un moyen populaire de résoudre le positionnement à partir d'images. Nous tirons parti de Google Maps et utilisons ses données topo-métriques hors ligne pour construire un positionnement grossier à fin, à savoir un processus de reconnaissance de lieu topologique puis une estimation métrique de pose par optimisation de graphe. La méthode a été testée en environnement urbain et démontre à la fois une précision sous-métrique et une robustesse aux changements de point de vue, à l'illumination et à l'occlusion. Aussi, les résultats montrent que les emplacements éloignés de Street Views produisent une erreur significative dans la phase d'estimation métrique. Ainsi, nous proposons de synthétiser des Street Views artificielles pour compenser la densité des Street View originales et améliorer la précision.Cette méthode souffre malheureusement d'un temps de calcul important. Étant donné que le SIG nous offre une base de données géolocalisée à l'échelle mondiale, cela nous motive à régresser des localisations globales directement à partir d'un convnet de bout en bout. La base de données hors ligne précédemment construite est encore insuffisante pour l'apprentissage d'un convnet. Pour compenser cela nous densifions la base d'origine d'un facteur mille et utilisons la méthode d'apprentissage par transfert pour faire converger notre régresseur convnet et avoir une bonne performance. Le régresseur permet également d'obtenir une localisation globale à partir d'une seule image et en temps réel.Les résultats obtenus par ces deux approches nous fournissent des informations sur la comparaison et la relation entre les méthodes basées sur des caractéristiques et celles basées sur le convnet. Après avoir analysé et comparé les performances de localisation des deux méthodes, nous avons également abordé des perspectives pour améliorer la robustesse et la précision de la localisation face au problème de localisation urbaine assistée par SIG. / In a work made at Centre de Robotique and Institut VEDECOM, we studied robust visual urban localization systems for self-driving cars. Obtaining an exact pose from a monocular camera is difficult and cannot be applied to the current autonomous cars. We mainly focused on fully leveraging Geographical Information Systems (GIS) to achieve a low-cost, robust, accurate and global urban localization.The development of public GIS's has brought us a new horizon to address the localization problem but their tremendous amount of information, such as topological, semantic, metric maps, Street Views, depth maps, 3D cadastral maps and High Definition maps, has to be well analyzed and organized to extract relevant information for self-driving cars. Our first task was to design a robotic accessible offline database from a dense public GIS, namely Google Maps, which has the advantage to propose a worldwide coverage. We make a compact topometric representation for the dynamic urban environment by extracting four useful data from the GIS, including topologies, geo-coordinates, panoramic Street Views, and associated depth maps. At the same time, an online dataset was acquired with a low-cost camera equipped on VEDECOM vehicles. In order to make spheric Street Views compatible with the online imagery, an image warping and interpolation based transformation is introduced to render rectilinear images from Street Views.We proposed two localization methods: one is a handcrafted-features-based computer vision approach, the other is a convolutional neural network (convnet) based learning technique. In computer vision, extracting handcrafted features is a popular way to solve the image based positioning. We take advantages of the abundant sources from Google Maps and benefit from the topometric offline data structure to build a coarse-to-fine positioning, namely a topological place recognition process and then a metric pose estimation by a graph optimization. The method is tested on an urban environment and demonstrates both sub-meter accuracy and robustness to viewpoint changes, illumination and occlusion. Moreover, we demonstrate that sparse Street View locations produce a significant error in the metric pose estimation phase. Thus our former framework is refined by synthesizing more artificial Street Views to compensate the sparsity of original Street Views and improve the precision.The handcrafted feature based framework requires the image retrieval and graph optimization. It is hard to achieve in a real-time application. Since the GIS offers us a global scale geotagged database, it motivates us to regress global localizations from convnet features in an end-to-end manner. The previously constructed offline database is still insufficient for a convnet training. We hereby augment the originally constructed database by a thousand factor and take advantage of the transfer learning method to make our convnet regressor converge and have a good performance. In our test, the regressor can also give a global localization of an input camera image in real time.The results obtained by the two approaches provide us insights on the comparison and connection between handcrafted feature-based and convnet based methods. After analyzing and comparing the localization performances of both methods, we also talked about some perspectives to improve the localization robustness and precision towards the GIS-aided urban localization problem. Localisation absolue Street View Gis Handcrafted features Convnet Mono-Caméra Localization Street View Gis Handcrafted features Convnet Mono-Camera 006.37
4	Vehicle Detection in Monochrome Images Lundagårds, Marcus January 2008 (has links) <p>The purpose of this master thesis was to study computer vision algorithms for vehicle detection in monochrome images captured by mono camera. The work has mainly been focused on detecting rear-view cars in daylight conditions. Previous work in the literature have been revised and algorithms based on edges, shadows and motion as vehicle cues have been modified, implemented and evaluated. This work presents a combination of a multiscale edge based detection and a shadow based detection as the most promising algorithm, with a positive detection rate of 96.4% on vehicles at a distance of between 5 m to 30 m. For the algorithm to work in a complete system for vehicle detection, future work should be focused on developing a vehicle classifier to reject false detections.</p> vehicle detection edge based detection shadow based detection motion based detection mono camera system Image analysis Bildanalys
5	Vehicle Detection in Monochrome Images Lundagårds, Marcus January 2008 (has links) The purpose of this master thesis was to study computer vision algorithms for vehicle detection in monochrome images captured by mono camera. The work has mainly been focused on detecting rear-view cars in daylight conditions. Previous work in the literature have been revised and algorithms based on edges, shadows and motion as vehicle cues have been modified, implemented and evaluated. This work presents a combination of a multiscale edge based detection and a shadow based detection as the most promising algorithm, with a positive detection rate of 96.4% on vehicles at a distance of between 5 m to 30 m. For the algorithm to work in a complete system for vehicle detection, future work should be focused on developing a vehicle classifier to reject false detections. vehicle detection edge based detection shadow based detection motion based detection mono camera system
6	Utvärdering av noggrannheten av kastparablar på en iPad / Accuracy evaluation of trajectories on an iPad Waninger, Mikael, Rothman, Sofia January 2022 (has links) Prestationsmätning och analys används inom sporter för att förbättra en spelares resultat relaterade till sin respektive sport. För analys finns labb och/eller dyr utrustning vilket gör den svårtillgänglig för icke-professionella utövare. Att minska kostnaden för mätverktyg bidrar till mer jämlika förutsättningar för spelare oavsett inkomst eller ålder. Den här studien syftar till att undersöka om en smartphone eller surfplatta kan användas för mätning och sportanalys. För att utforska detta utvecklades en applikation med fokus på projektilsporter som fotboll, tennis och golf. Applikationen testar visualisering av ett objekts parabel, mätning av dess hastighet och visualisering av dess träff i ett vertikalt plan. Applikationen utvecklades för iOS och testades på en iPad 12 pro. Tester för att validera applikationens noggrannhet utfördes med en fotboll, en tennisboll och en golfboll. Testresultaten för visualisering av parabel gav resultat för fotboll och tennisboll men kunde inte hantera golfbollens mindre storlek. Hastighet kunde mätas för alla tre bollar med en genomsnittlig procentuell avvikelse på 76% för fotboll, 21% för tennisboll och 43% för golfboll. Testresultaten för visualisering av ett objekts träff i ett målplan visade resultat för fotboll och tennisboll, men inte för en golfboll. Den genomsnittliga procentuella avvikelsen var 89% för fotboll respektive 23% för tennisboll. / Measuring and analyzing player performance within sports helps to improve a players results in regards to their respective sport. Specialized labs and or expensive equipment are used for analysis but are difficult to access for the average player. Decreasing the cost of measurement tools would help equalize the playing field for players regardless of age or economic background. This study evaluates if a smartphone or tablet can be used to perform the same task. To achieve this an application was developed with a focus on projectile sports such as soccer, tennis, or golf. The application will visualize a parabola, measure speed, and visualize the point where an object hits a vertical plane. The application was developed for iOS and was tested on an iPad 12 pro. The tests were performed with a soccer ball, tennis ball and golf ball. Tests for visualizing a parabola produced results for the soccer ball and the tennis ball but could not handle the golf balls smaller size. Speed was measured for all three balls with an average percentual offset of 76% for the soccer ball, 21% for the tennis ball and 43% for the golf ball. Hit on a vertical plan produced results for the soccer ball and tennis ball with an average percentual offset of 89% for the soccer ball and 23% for the tennis ball. sport analysis iOS object tracking object detection augmented reality mono camera system sportanalys iOS objektspårning objektdetektering förstärkt verklighet monokamerasystem Computer Engineering Datorteknik
7	Road Surface Preview Estimation Using a Monocular Camera Ekström, Marcus January 2018 (has links) Recently, sensors such as radars and cameras have been widely used in automotives, especially in Advanced Driver-Assistance Systems (ADAS), to collect information about the vehicle's surroundings. Stereo cameras are very popular as they could be used passively to construct a 3D representation of the scene in front of the car. This allowed the development of several ADAS algorithms that need 3D information to perform their tasks. One interesting application is Road Surface Preview (RSP) where the task is to estimate the road height along the future path of the vehicle. An active suspension control unit can then use this information to regulate the suspension, improving driving comfort, extending the durabilitiy of the vehicle and warning the driver about potential risks on the road surface. Stereo cameras have been successfully used in RSP and have demonstrated very good performance. However, the main disadvantages of stereo cameras are their high production cost and high power consumption. This limits installing several ADAS features in economy-class vehicles. A less expensive alternative are monocular cameras which have a significantly lower cost and power consumption. Therefore, this thesis investigates the possibility of solving the Road Surface Preview task using a monocular camera. We try two different approaches: structure-from-motion and Convolutional Neural Networks.The proposed methods are evaluated against the stereo-based system. Experiments show that both structure-from-motion and CNNs have a good potential for solving the problem, but they are not yet reliable enough to be a complete solution to the RSP task and be used in an active suspension control unit. Road Surface Preview Computer Vision Depth Estimation Convolutional Neural Network CNN traffic safety monocular camera mono vision system mono camera Structure from motion sfm 3D Reconstruction Autonomous Driving Datorseende trafiksäkerhet djupuppskattning mono kamera 3D rekonstruktion autonoma fordon Signal Processing Signalbehandling
8	Deep Convolutional Neural Networks for Real-Time Single Frame Monocular Depth Estimation Schennings, Jacob January 2017 (has links) Vision based active safety systems have become more frequently occurring in modern vehicles to estimate depth of the objects ahead and for autonomous driving (AD) and advanced driver-assistance systems (ADAS). In this thesis a lightweight deep convolutional neural network performing real-time depth estimation on single monocular images is implemented and evaluated. Many of the vision based automatic brake systems in modern vehicles only detect pre-trained object types such as pedestrians and vehicles. These systems fail to detect general objects such as road debris and roadside obstacles. In stereo vision systems the problem is resolved by calculating a disparity image from the stereo image pair to extract depth information. The distance to an object can also be determined using radar and LiDAR systems. By using this depth information the system performs necessary actions to avoid collisions with objects that are determined to be too close. However, these systems are also more expensive than a regular mono camera system and are therefore not very common in the average consumer car. By implementing robust depth estimation in mono vision systems the benefits from active safety systems could be utilized by a larger segment of the vehicle fleet. This could drastically reduce human error related traffic accidents and possibly save many lives. The network architecture evaluated in this thesis is more lightweight than other CNN architectures previously used for monocular depth estimation. The proposed architecture is therefore preferable to use on computationally lightweight systems. The network solves a supervised regression problem during the training procedure in order to produce a pixel-wise depth estimation map. The network was trained using a sparse ground truth image with spatially incoherent and discontinuous data and output a dense spatially coherent and continuous depth map prediction. The spatially incoherent ground truth posed a problem of discontinuity that was addressed by a masked loss function with regularization. The network was able to predict a dense depth estimation on the KITTI dataset with close to state-of-the-art performance. deep learning machine learning mono vision system lightweight CNN convolutional neural network depth estimation lidar kitti vehicle camera mono camera camera real-time real time ad autonomous driving adas advanced driver assistance systems mono depth computer vision regression pixel-wise pixel wise object detection general object detection pedestrian detection vehicle detection supervised learning supervised tensorflow python keras opencv autoliv
9	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