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1	Human Motion Tracking Using 3D Camera / Följning av människa med 3D-kamera Karlsson, Daniel January 2010 (has links) <p>The interest in video surveillance has increased in recent years. Cameras are now installed in e.g. stores, arenas and prisons. The video data is analyzed to detect abnormal or undesirable events such as thefts, fights and escapes. At the Informatics Unit at the division of Information Systems, FOI in Linköping, algorithms are developed for automatic detection and tracking of humans in video data. This thesis deals with the target tracking problem when a 3D camera is used. A 3D camera creates images whose pixels represent the ranges to the scene. In recent years, new camera systems have emerged where the range images are delivered at up to video rate (30 Hz). One goal of the thesis is to determine how range data affects the frequency with which the measurement update part of the tracking algorithm must be performed. Performance of the 2D tracker and the 3D tracker are evaluated with both simulated data and measured data from a 3D camera. It is concluded that the errors in the estimated image coordinates are independent of whether range data is available or not. The small angle and the relatively large distance to the target explains the good performance of the 2D tracker. The 3D tracker however shows superior tracking ability (much smaller tracking error) if the comparison is made in the world coordinates.</p> human motion tracking 3D tracking 3D camera LIDAR LADAR RIM EKF TOF följning av människa 3D-målföljning 3D-kamera avståndskamera LIDAR LADAR RIM EKF TOF Automatic control Reglerteknik Signal processing Signalbehandling
2	Human Motion Tracking Using 3D Camera / Följning av människa med 3D-kamera Karlsson, Daniel January 2010 (has links) The interest in video surveillance has increased in recent years. Cameras are now installed in e.g. stores, arenas and prisons. The video data is analyzed to detect abnormal or undesirable events such as thefts, fights and escapes. At the Informatics Unit at the division of Information Systems, FOI in Linköping, algorithms are developed for automatic detection and tracking of humans in video data. This thesis deals with the target tracking problem when a 3D camera is used. A 3D camera creates images whose pixels represent the ranges to the scene. In recent years, new camera systems have emerged where the range images are delivered at up to video rate (30 Hz). One goal of the thesis is to determine how range data affects the frequency with which the measurement update part of the tracking algorithm must be performed. Performance of the 2D tracker and the 3D tracker are evaluated with both simulated data and measured data from a 3D camera. It is concluded that the errors in the estimated image coordinates are independent of whether range data is available or not. The small angle and the relatively large distance to the target explains the good performance of the 2D tracker. The 3D tracker however shows superior tracking ability (much smaller tracking error) if the comparison is made in the world coordinates. human motion tracking 3D tracking 3D camera LIDAR LADAR RIM EKF TOF följning av människa 3D-målföljning 3D-kamera avståndskamera LIDAR LADAR RIM EKF TOF Automatic control Reglerteknik Signal processing Signalbehandling
3	Tracking a ball during bounce and roll using recurrent neural networks / Följning av en boll under studs och rull med hjälp av återkopplande neurala nätverk Rosell, Felicia January 2018 (has links) In many types of sports, on-screen graphics such as an reconstructed ball trajectory, can be displayed for spectators or players in order to increase understanding. One sub-problem of trajectory reconstruction is tracking of ball positions, which is a difficult problem due to the fast and often complex ball movement. Historically, physics based techniques have been used to track ball positions, but this thesis investigates using a recurrent neural network design, in the application of tracking bouncing golf balls. The network is trained and tested on synthetically created golf ball shots, created to imitate balls shot out from a golf driving range. It is found that the trained network succeeds in tracking golf balls during bounce and roll, with an error rate of under 11 %. / Grafik visad på en skärm, så som en rekonstruerad bollbana, kan användas i många typer av sporter för att öka en åskådares eller spelares förståelse. För att lyckas rekonstruera bollbanor behöver man först lösa delproblemet att följa en bolls positioner. Följning av bollpositioner är ett svårt problem på grund av den snabba och ofta komplexa bollrörelsen. Tidigare har fysikbaserade tekniker använts för att följa bollpositioner, men i den här uppsatsen undersöks en metod baserad på återkopplande neurala nätverk, för att följa en studsande golfbolls bana. Nätverket tränas och testas på syntetiskt skapade golfslag, där bollbanorna är skapade för att imitera golfslag från en driving range. Efter träning lyckades nätverket följa golfbollar under studs och rull med ett fel på under 11 %. machine learning ML recurrent neural networks RNN deep learning tracking golf bounce synthetic data maskininlärning ML recurrent neural networks RNN djupinlärning följning golf studs syntetiskt data Computer Sciences Datavetenskap (datalogi)
4	Incorporating Scene Depth in Discriminative Correlation Filters for Visual Tracking Stynsberg, John January 2018 (has links) Visual tracking is a computer vision problem where the task is to follow a targetthrough a video sequence. Tracking has many important real-world applications in several fields such as autonomous vehicles and robot-vision. Since visual tracking does not assume any prior knowledge about the target, it faces different challenges such occlusion, appearance change, background clutter and scale change. In this thesis we try to improve the capabilities of tracking frameworks using discriminative correlation filters by incorporating scene depth information. We utilize scene depth information on three main levels. First, we use raw depth information to segment the target from its surroundings enabling occlusion detection and scale estimation. Second, we investigate different visual features calculated from depth data to decide which features are good at encoding geometric information available solely in depth data. Third, we investigate handling missing data in the depth maps using a modified version of the normalized convolution framework. Finally, we introduce a novel approach for parameter search using genetic algorithms to find the best hyperparameters for our tracking framework. Experiments show that depth data can be used to estimate scale changes and handle occlusions. In addition, visual features calculated from depth are more representative if they were combined with color features. It is also shown that utilizing normalized convolution improves the overall performance in some cases. Lastly, the usage of genetic algorithms for hyperparameter search leads to accuracy gains as well as some insights on the performance of different components within the framework. Tracking Visual Deep Learning Machine Learning CNN Convolutional Neural Network Unsupervised Learning Clustering Genetic Algorithms Features Visual featues Channel Coding RGBD Scene Depth Map Kinect Discriminative Correlation Filters SRDCF DCF Spatial Spatially Regularized Hyperparameter Search Occlusion Detection Handling Kalman Filters Normalized Convolution Bayesian Gaussian Mixture Scale Estimation Conjugate Gradient Linkoping Sweden Visuell Följning Särdrag Djupa Faltningsnätverk Maskininlärning Djup Inlärning Genetiska Algoritmer Klustring Djup RGBD Linköping Sverige

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