Relevant bibliographies by topics / Bayesian target tracking

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Bayesian target tracking'

Author: Grafiati
Published: 4 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "Bayesian target tracking"

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Vivone, Gemine, Paolo Braca, Karl Granstrom, and Peter Willett. "Multistatic Bayesian extended target tracking." IEEE Transactions on Aerospace and Electronic Systems 52, no. 6 (2016): 2626–43. http://dx.doi.org/10.1109/taes.2016.150724.

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Sun, Kang. "New Compound Method for Target Recognition and Tracking." Applied Mechanics and Materials 273 (January 2013): 790–95. http://dx.doi.org/10.4028/www.scientific.net/amm.273.790.

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In this paper, we propose compound target detection and tracking method that combines Bayesian local features classification and global template tracking. During target initialization phase, we convert local features recognition problem into Semi-Naive Bayesian classification theory to avoid computing and matching complex high-dimension descriptor. During tracking, detector hands over tracking task to the template tracker, which imposes temporal continuity constraints across on-line frames in order to increase the robustness and efficiency of the results. In typical application scenarios, once the tracker loses target, it requires the detector for reinitialization. Experiment results confirm the efficiency of our approach at last.
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Quan, Hong Wei, Jun Hua Li, and Xiao Juan Zhang. "Joint Target Detection Tracking and Classification Based on Finite-Set Statistics Theory." Applied Mechanics and Materials 668-669 (October 2014): 1072–75. http://dx.doi.org/10.4028/www.scientific.net/amm.668-669.1072.

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In traditional target tracking methods, the target number, target states and target class can not be estimated in same time. This paper investigated the joint target detection, tracking and classification method which is based on finite-set statistics theory. First, the random set and finite-set statistics theory are introduced for theoretic analysis. Second, the finite-set model for target tracking is given to construct a generalized nonlinear fusion framework. Finally, the finite-set based Bayesian filter is developed to track the targets in surveillance region. By recursively calculating the probabilistic hypothesis density, the target number, target states and target class can be evaluated simultaneously.
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Stone, Larry, Roy Streit, Tom Corwin, Kristine Bell, and Fred Daum. "Bayesian multiple target tracking, 2nd edition [Book review]." IEEE Aerospace and Electronic Systems Magazine 29, no. 8 (2014): 23–24. http://dx.doi.org/10.1109/maes.2014.140049.

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Li, Xiaohua, Bo Lu, Wasiq Ali, and Haiyan Jin. "Passive Tracking of Multiple Underwater Targets in Incomplete Detection and Clutter Environment." Entropy 23, no. 8 (2021): 1082. http://dx.doi.org/10.3390/e23081082.

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A major advantage of the use of passive sonar in the tracking multiple underwater targets is that they can be kept covert, which reduces the risk of being attacked. However, the nonlinearity of the passive Doppler and bearing measurements, the range unobservability problem, and the complexity of data association between measurements and targets make the problem of underwater passive multiple target tracking challenging. To deal with these problems, the cardinalized probability hypothesis density (CPHD) recursion, which is based on Bayesian information theory, is developed to handle the data association uncertainty, and to acquire existing targets’ numbers and states (e.g., position and velocity). The key idea of the CPHD recursion is to simultaneously estimate the targets’ intensity and the probability distribution of the number of targets. The CPHD recursion is the first moment approximation of the Bayesian multiple targets filter, which avoids the data association procedure between the targets and measurements including clutter. The Bayesian-filter-based extended Kalman filter (EKF) is applied to deal with the nonlinear bearing and Doppler measurements. The experimental results show that the EKF-based CPHD recursion works well in the underwater passive multiple target tracking system in cluttered and noisy environments.
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Haoxiang, Dr Wang, and Dr Smys S. "WSN based Improved Bayesian Algorithm Combined with Enhanced Least-Squares Algorithm for Target Localizing and Tracking." IRO Journal on Sustainable Wireless Systems 2, no. 2 (2020): 59–67. http://dx.doi.org/10.36548/jsws.2020.2.001.

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For wireless sensor network (WSN), localization and tracking of targets are implemented extensively by means of traditional tracking algorithms like classical least-square (CLS) algorithm, extended Kalman filter (EKF) and the Bayesian algorithm. For the purpose of tracking and moving target localization of WSN, this paper proposes an improved Bayesian algorithm that combines the principles of least-square algorithm. For forming a matrix of range joint probability and using target predictive location of obtaining a sub-range probability set, an improved Bayesian algorithm is implemented. During the dormant state of the WSN testbed, an automatic update of the range joint probability matrix occurs. Further, the range probability matrix is used for the calculation and normalization of the weight of every individual measurement. Lastly, based on the weighted least-square algorithm, calculation of the target prediction position and its correction value is performed. The accuracy of positioning of the proposed algorithm is improved when compared to variational Bayes expectation maximization (VBEM), dual-factor enhanced VBAKF (EVBAKF), variational Bayesian adaptive Kalman filtering (VBAKF), the fingerprint Kalman filter (FKF), the position Kalman filter (PKF), the weighted K-nearest neighbor (WKNN) and the EKF algorithms with the values of 0.5%, 7%, 14%, 19%, 33% and 35% respectively. Along with this, when compared to Bayesian algorithm, the computation burden is reduced by the proposed algorithm by a factor of over 80%.
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Xiong, Wei, Xiangqi Gu, and Yaqi Cui. "Tracking and Data Association Based on Reinforcement Learning." Electronics 12, no. 11 (2023): 2388. http://dx.doi.org/10.3390/electronics12112388.

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Currently, most multi-target data association methods require the assumption that the target motion model is known, but this assumption is clearly not valid in a real environment. In the case of an unknown system model, the influence of environmental clutter and sensor detection errors on the association results should be considered, as well as the occurrence of strong target maneuvers and the sudden appearance of new targets during the association process. To address these problems, this paper designs a target tracking and data association algorithm based on reinforcement learning. First, this algorithm combines the dynamic exploration capability of reinforcement learning and the long-time memory function of LSTM network to design a policy network that predicts the probability of associating a point with its various possible source targets. Then, the Bayesian network and the multi-order least squares curve fitting method are combined to predict the location of target, and the results are fed into the Bayesian recursive function to obtain the reward. Simultaneously, some corresponding mechanisms are proposed for possible problems that interfere with the association process. Finally, the simulation experimental results show that this algorithm associates the results with higher accuracy compared to other algorithms when faced with the above problem.
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Lan Jiang, Sumeetpal S. Singh, and Sinan Yildirim. "Bayesian Tracking and Parameter Learning for Non-Linear Multiple Target Tracking Models." IEEE Transactions on Signal Processing 63, no. 21 (2015): 5733–45. http://dx.doi.org/10.1109/tsp.2015.2454474.

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Taguchi, Shun, and Kiyosumi Kidono. "Exclusive Association Sampling to Improve Bayesian Multi-Target Tracking." IEEE Access 8 (2020): 193116–27. http://dx.doi.org/10.1109/access.2020.3032692.

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Kumar, Pankaj, and Anthony Dick. "Adaptive earth movers distance‐based Bayesian multi‐target tracking." IET Computer Vision 7, no. 4 (2013): 246–57. http://dx.doi.org/10.1049/iet-cvi.2011.0223.

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Dissertations / Theses on the topic "Bayesian target tracking"

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Biresaw, Tewodros Atanaw. "Self-correcting Bayesian target tracking." Thesis, Queen Mary, University of London, 2015. http://qmro.qmul.ac.uk/xmlui/handle/123456789/7925.

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Visual tracking, a building block for many applications, has challenges such as occlusions,illumination changes, background clutter and variable motion dynamics that may degrade the tracking performance and are likely to cause failures. In this thesis, we propose Track-Evaluate-Correct framework (self-correlation) for existing trackers in order to achieve a robust tracking. For a tracker in the framework, we embed an evaluation block to check the status of tracking quality and a correction block to avoid upcoming failures or to recover from failures. We present a generic representation and formulation of the self-correcting tracking for Bayesian trackers using a Dynamic Bayesian Network (DBN). The self-correcting tracking is done similarly to a selfaware system where parameters are tuned in the model or different models are fused or selected in a piece-wise way in order to deal with tracking challenges and failures. In the DBN model representation, the parameter tuning, fusion and model selection are done based on evaluation and correction variables that correspond to the evaluation and correction, respectively. The inferences of variables in the DBN model are used to explain the operation of self-correcting tracking. The specific contributions under the generic self-correcting framework are correlation-based selfcorrecting tracking for an extended object with model points and tracker-level fusion as described below. For improving the probabilistic tracking of extended object with a set of model points, we use Track-Evaluate-Correct framework in order to achieve self-correcting tracking. The framework combines the tracker with an on-line performance measure and a correction technique. We correlate model point trajectories to improve on-line the accuracy of a failed or an uncertain tracker. A model point tracker gets assistance from neighbouring trackers whenever degradation in its performance is detected using the on-line performance measure. The correction of the model point state is based on the correlation information from the states of other trackers. Partial Least Square regression is used to model the correlation of point tracker states from short windowed trajectories adaptively. Experimental results on data obtained from optical motion capture systems show the improvement in tracking performance of the proposed framework compared to the baseline tracker and other state-of-the-art trackers. The proposed framework allows appropriate re-initialisation of local trackers to recover from failures that are caused by clutter and missed detections in the motion capture data. Finally, we propose a tracker-level fusion framework to obtain self-correcting tracking. The fusion framework combines trackers addressing different tracking challenges to improve the overall performance. As a novelty of the proposed framework, we include an online performance measure to identify the track quality level of each tracker to guide the fusion. The trackers in the framework assist each other based on appropriate mixing of the prior states. Moreover, the track quality level is used to update the target appearance model. We demonstrate the framework with two Bayesian trackers on video sequences with various challenges and show its robustness compared to the independent use of the trackers used in the framework, and also compared to other state-of-the-art trackers. The appropriate online performance measure based appearance model update and prior mixing on trackers allows the proposed framework to deal with tracking challenges.
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Gordon, Neil. "Bayesian methods for tracking." Thesis, Imperial College London, 1993. http://hdl.handle.net/10044/1/7783.

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Cevher, Volkan. "A Bayesian Framework for Target Tracking using Acoustic and Image Measurements." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/6824.

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Target tracking is a broad subject area extensively studied in many engineering disciplines. In this thesis, target tracking implies the temporal estimation of target features such as the target's direction-of-arrival (DOA), the target's boundary pixels in a sequence of images, and/or the target's position in space. For multiple target tracking, we have introduced a new motion model that incorporates an acceleration component along the heading direction of the target. We have also shown that the target motion parameters can be considered part of a more general feature set for target tracking, e.g., target frequencies, which may be unrelated to the target motion, can be used to improve the tracking performance. We have introduced an acoustic multiple-target tracker using a flexible observation model based on an image tracking approach by assuming that the DOA observations might be spurious and that some of the DOAs might be missing in the observation set. We have also addressed the acoustic calibration problem from sources of opportunity such as beacons or a moving source. We have derived and compared several calibration methods for the case where the node can hear a moving source whose position can be reported back to the node. The particle filter, as a recursive algorithm, requires an initialization phase prior to tracking a state vector. The Metropolis-Hastings (MH) algorithm has been used for sampling from intractable multivariate target distributions and is well suited for the initialization problem. Since the particle filter only needs samples around the mode, we have modified the MH algorithm to generate samples distributed around the modes of the target posterior. By simulations, we show that this mode hungry algorithm converges an order of magnitude faster than the original MH scheme. Finally, we have developed a general framework for the joint state-space tracking problem. A proposal strategy for joint state-space tracking using the particle filters is defined by carefully placing the random support of the joint filter in the region where the final posterior is likely to lie. Computer simulations demonstrate improved performance and robustness of the joint state-space when using the new particle proposal strategy.
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Papakis, Ioannis. "A Bayesian Framework for Multi-Stage Robot, Map and Target Localization." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/93024.

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This thesis presents a generalized Bayesian framework for a mobile robot to localize itself and a target, while building a map of the environment. The proposed technique builds upon the Bayesian Simultaneous Robot Localization and Mapping (SLAM) method, to allow the robot to localize itself and the environment using map features or landmarks in close proximity. The target feature is distinguished from the rest of features since the robot has to navigate to its location and thus needs to be observed from a long distance. The contribution of the proposed approach is on enabling the robot to track a target object or region, using a multi-stage technique. In the first stage, the target state is corrected sequentially to the robot correction in the Recursive Bayesian Estimation. In the second stage, with the target being closer, the target state is corrected simultaneously with the robot and the landmarks. The process allows the robot's state uncertainty to be propagated into the estimated target's state, bridging the gap between tracking only methods where the target is estimated assuming known observer state and SLAM methods where only landmarks are considered. When the robot is located far, the sequential stage is efficient in tracking the target position while maintaining an accurate robot state using close only features. Also, target belief is always maintained in comparison to temporary tracking methods such as image-tracking. When the robot is closer to the target and most of its field of view is covered by the target, it is shown that simultaneous correction needs to be used in order to minimize robot, target and map entropies in the absence of other landmarks.<br>M.S.<br>This thesis presents a generalized framework with the goal of allowing a robot to localize itself and a static target, while building a map of the environment. This map is used as in the Simultaneous Localization and Mapping (SLAM) framework to enhance robot accuracy and with close features. Target, here, is distinguished from the rest of features since the robot has to navigate to its location and thus needs to be continuously observed from a long distance. The contribution of the proposed approach is on enabling the robot to track a target object or region, using a multi-stage technique. In the first stage, the robot and close landmarks are estimated simultaneously and they are both corrected. Using the robot's uncertainty in its estimate, the target state is then estimated sequentially, considering known robot state. That decouples the target estimation from the rest of the process. In the second stage, with the target being closer, target, robot and landmarks are estimated simultaneously. When the robot is located far, the sequential stage is efficient in tracking the target position while maintaining an accurate robot state using close only features. When the robot is closer to the target and most of its field of view is covered by the target, it is shown that simultaneous correction needs to be used in order to minimize robot, target and map uncertainties in the absence of other landmarks.
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Stein, Andrew Neil. "Adaptive image segmentation and tracking : a Bayesian approach." Thesis, Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/13397.

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ur-Rehman, Ata. "Bayesian-based techniques for tracking multiple humans in an enclosed environment." Thesis, Loughborough University, 2014. https://dspace.lboro.ac.uk/2134/14174.

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This thesis deals with the problem of online visual tracking of multiple humans in an enclosed environment. The focus is to develop techniques to deal with the challenges of varying number of targets, inter-target occlusions and interactions when every target gives rise to multiple measurements (pixels) in every video frame. This thesis contains three different contributions to the research in multi-target tracking. Firstly, a multiple target tracking algorithm is proposed which focuses on mitigating the inter-target occlusion problem during complex interactions. This is achieved with the help of a particle filter, multiple video cues and a new interaction model. A Markov chain Monte Carlo particle filter (MCMC-PF) is used along with a new interaction model which helps in modeling interactions of multiple targets. This helps to overcome tracking failures due to occlusions. A new weighted Markov chain Monte Carlo (WMCMC) sampling technique is also proposed which assists in achieving a reduced tracking error. Although effective, to accommodate multiple measurements (pixels) produced by every target, this technique aggregates measurements into features which results in information loss. In the second contribution, a novel variational Bayesian clustering-based multi-target tracking framework is proposed which can associate multiple measurements to every target without aggregating them into features. It copes with complex inter-target occlusions by maintaining the identity of targets during their close physical interactions and handles efficiently a time-varying number of targets. The proposed multi-target tracking framework consists of background subtraction, clustering, data association and particle filtering. A variational Bayesian clustering technique groups the extracted foreground measurements while an improved feature based joint probabilistic data association filter (JPDAF) is developed to associate clusters of measurements to every target. The data association information is used within the particle filter to track multiple targets. The clustering results are further utilised to estimate the number of targets. The proposed technique improves the tracking accuracy. However, the proposed features based JPDAF technique results in an exponential growth of computational complexity of the overall framework with increase in number of targets. In the final work, a novel data association technique for multi-target tracking is proposed which more efficiently assigns multiple measurements to every target, with a reduced computational complexity. A belief propagation (BP) based cluster to target association method is proposed which exploits the inter-cluster dependency information. Both location and features of clusters are used to re-identify the targets when they emerge from occlusions. The proposed techniques are evaluated on benchmark data sets and their performance is compared with state-of-the-art techniques by using, quantitative and global performance measures.
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Ozkan, Emre. "Particle Methods For Bayesian Multi-object Tracking And Parameter Estimation." Phd thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/3/12610986/index.pdf.

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In this thesis a number of improvements have been established for specific methods which utilize sequential Monte Carlo (SMC), aka. Particle filtering (PF) techniques. The first problem is the Bayesian multi-target tracking (MTT) problem for which we propose the use of non-parametric Bayesian models that are based on time varying extension of Dirichlet process (DP) models. The second problem studied in this thesis is an important application area for the proposed DP based MTT method<br>the tracking of vocal tract resonance frequencies of the speech signals. Lastly, we investigate SMC based parameter estimation problem of nonlinear non-Gaussian state space models in which we provide a performance improvement for the path density based methods by utilizing regularization techniques.
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Daniyan, Abdullahi. "Advanced signal processing techniques for multi-target tracking." Thesis, Loughborough University, 2018. https://dspace.lboro.ac.uk/2134/35277.

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The multi-target tracking problem essentially involves the recursive joint estimation of the state of unknown and time-varying number of targets present in a tracking scene, given a series of observations. This problem becomes more challenging because the sequence of observations is noisy and can become corrupted due to miss-detections and false alarms/clutter. Additionally, the detected observations are indistinguishable from clutter. Furthermore, whether the target(s) of interest are point or extended (in terms of spatial extent) poses even more technical challenges. An approach known as random finite sets provides an elegant and rigorous framework for the handling of the multi-target tracking problem. With a random finite sets formulation, both the multi-target states and multi-target observations are modelled as finite set valued random variables, that is, random variables which are random in both the number of elements and the values of the elements themselves. Furthermore, compared to other approaches, the random finite sets approach possesses a desirable characteristic of being free of explicit data association prior to tracking. In addition, a framework is available for dealing with random finite sets and is known as finite sets statistics. In this thesis, advanced signal processing techniques are employed to provide enhancements to and develop new random finite sets based multi-target tracking algorithms for the tracking of both point and extended targets with the aim to improve tracking performance in cluttered environments. To this end, firstly, a new and efficient Kalman-gain aided sequential Monte Carlo probability hypothesis density (KG-SMC-PHD) filter and a cardinalised particle probability hypothesis density (KG-SMC-CPHD) filter are proposed. These filters employ the Kalman- gain approach during weight update to correct predicted particle states by minimising the mean square error between the estimated measurement and the actual measurement received at a given time in order to arrive at a more accurate posterior. This technique identifies and selects those particles belonging to a particular target from a given PHD for state correction during weight computation. The proposed SMC-CPHD filter provides a better estimate of the number of targets. Besides the improved tracking accuracy, fewer particles are required in the proposed approach. Simulation results confirm the improved tracking performance when evaluated with different measures. Secondly, the KG-SMC-(C)PHD filters are particle filter (PF) based and as with PFs, they require a process known as resampling to avoid the problem of degeneracy. This thesis proposes a new resampling scheme to address a problem with the systematic resampling method which causes a high tendency of resampling very low weight particles especially when a large number of resampled particles are required; which in turn affect state estimation. Thirdly, the KG-SMC-(C)PHD filters proposed in this thesis perform filtering and not tracking , that is, they provide only point estimates of target states but do not provide connected estimates of target trajectories from one time step to the next. A new post processing step using game theory as a solution to this filtering - tracking problem is proposed. This approach was named the GTDA method. This method was employed in the KG-SMC-(C)PHD filter as a post processing technique and was evaluated using both simulated and real data obtained using the NI-USRP software defined radio platform in a passive bi-static radar system. Lastly, a new technique for the joint tracking and labelling of multiple extended targets is proposed. To achieve multiple extended target tracking using this technique, models for the target measurement rate, kinematic component and target extension are defined and jointly propagated in time under the generalised labelled multi-Bernoulli (GLMB) filter framework. The GLMB filter is a random finite sets-based filter. In particular, a Poisson mixture variational Bayesian (PMVB) model is developed to simultaneously estimate the measurement rate of multiple extended targets and extended target extension was modelled using B-splines. The proposed method was evaluated with various performance metrics in order to demonstrate its effectiveness in tracking multiple extended targets.
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Brau, Avila Ernesto. "Bayesian Data Association for Temporal Scene Understanding." Diss., The University of Arizona, 2013. http://hdl.handle.net/10150/312653.

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Understanding the content of a video sequence is not a particularly difficult problem for humans. We can easily identify objects, such as people, and track their position and pose within the 3D world. A computer system that could understand the world through videos would be extremely beneficial in applications such as surveillance, robotics, biology. Despite significant advances in areas like tracking and, more recently, 3D static scene understanding, such a vision system does not yet exist. In this work, I present progress on this problem, restricted to videos of objects that move in smoothly and which are relatively easily detected, such as people. Our goal is to identify all the moving objects in the scene and track their physical state (e.g., their 3D position or pose) in the world throughout the video. We develop a Bayesian generative model of a temporal scene, where we separately model data association, the 3D scene and imaging system, and the likelihood function. Under this model, the video data is the result of capturing the scene with the imaging system, and noisily detecting video features. This formulation is very general, and can be used to model a wide variety of scenarios, including videos of people walking, and time-lapse images of pollen tubes growing in vitro. Importantly, we model the scene in world coordinates and units, as opposed to pixels, allowing us to reason about the world in a natural way, e.g., explaining occlusion and perspective distortion. We use Gaussian processes to model motion, and propose that it is a general and effective way to characterize smooth, but otherwise arbitrary, trajectories. We perform inference using MCMC sampling, where we fit our model of the temporal scene to data extracted from the videos. We address the problem of variable dimensionality by estimating data association and integrating out all scene variables. Our experiments show our approach is competitive, producing results which are comparable to state-of-the-art methods.
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Bryan, Everett A. "Cooperative Target Tracking Enhanced with the Sequence Memoizer." BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/3814.

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Target tracking is an important part of video surveillance from a UAV. Tracking a target in an urban environment can be difficult because of the number of occlusions present in the environment. If multiple UAVs are used to track a target and the target behavior is learned autonomously by the UAV then the task may become easier. This thesis explores the hypothesis that an existing cooperative control algorithm can be enhanced by a language modeling algorithm to improve over time the target tracking performance of one or more ground targets in a dense urban environment. Observations of target behavior are reported to the Sequence Memoizer which uses the observations to create a belief model of future target positions. This belief model is combined with a kinematic belief model and then used in a cooperative auction algorithm for UAV path planning. The results for tracking a single target using the combined belief model outperform other belief models and improve over the duration of the mission. Results from tracking multiple targets indicate that algorithmic enhancements may be needed to find equivalent success. Future target tracking algorithms should involve machine learning to enhance tracking performance.
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Books on the topic "Bayesian target tracking"

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Bayesian Multiple Target Tracking. Artech House Publishers, 2014.

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Stone, Lawrence D., Carl A. Barlow, and Thomas L. Corwin. Bayesian Multiple Target Tracking (Artech House Radar Library). Artech House Publishers, 1999.

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Book chapters on the topic "Bayesian target tracking"

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Stone, Lawrence D., Roy L. Streit, and Stephen L. Anderson. "Bayesian Single Target Tracking." In Studies in Big Data. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32242-6_2.

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Vargas, Juan E., Kiran Tvalarparti, and Zhaojun Wu. "Target Tracking with Bayesian Estimation." In Multiagent Systems, Artificial Societies, and Simulated Organizations. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0363-7_5.

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Vo, Ba-Ngu, Ba-Tuong VO, and Daniel Clark. "Bayesian Multiple Target Filtering Using Random Finite Sets." In Integrated Tracking, Classification, and Sensor Management. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118450550.ch03.

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Manfredotti, Cristina, and Enza Messina. "Relational Dynamic Bayesian Networks to Improve Multi-target Tracking." In Advanced Concepts for Intelligent Vision Systems. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04697-1_49.

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Achutegui, Katrin, Javier Rodas, Carlos J. Escudero, and Joaquín Míguez. "Bayesian Filtering Methods for Target Tracking in Mixed Indoor/Outdoor Environments." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29479-2_13.

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Snoussi, Hichem, Paul Honeine, and Cédric Richard. "Kernel Variational Approach for Target Tracking in a Wireless Sensor Network." In Regularization and Bayesian Methods for Inverse Problems in Signal and Image Processing. John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781118827253.ch10.

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Elayaraja, Alonshia S. "Bayesian Localized Energy Optimized Sensor Distribution for Efficient Target Tracking." In Advances in Business Information Systems and Analytics. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5522-3.ch001.

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Many applications in wireless sensor networks perform localization of nodes over an extended period of time. Optimal selection algorithm poses new challenges to the overall transmission power levels for target detection, and thus, localized energy optimized sensor management strategies are necessary for improving the accuracy of target tracking. In this chapter, a proposal plan to develop a Bayesian localized energy optimized sensor distribution scheme for efficient target tracking in wireless sensor network is designed. The sensor node localization is done with Bayesian average, which estimates the sensor node's energy optimality. Then the sensor nodes are localized and distributed based on the Bayesian energy estimate for efficient target tracking. The sensor node distributional strategy improves the accuracy of identifying the targets to be tracked quickly. The performance is evaluated with parameters such as accuracy of target tracking, energy consumption rate, localized node density, and time for target tracking.
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"A Spherical Constant Velocity Model for Target Tracking in Three Dimensions." In Bayesian Estimation and Tracking. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118287798.ch18.

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Stone, Lawrence. "Bayesian Approach to Multiple-Target Tracking*." In Handbook of Multisensor Data Fusion. CRC Press, 2008. http://dx.doi.org/10.1201/9781420053098.ch12.

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Stone, Lawrence. "A Bayesian Approachto Multiple- Target Tracking*." In Multisensor Data Fusion, 2 Volume Set. CRC Press, 2001. http://dx.doi.org/10.1201/9781420038545.ch10.

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Conference papers on the topic "Bayesian target tracking"

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Yang, Huimin, and Man Chen. "Variational Bayesian-based multiextended target tracking." In 4th International Conference on Green Communication, Network, and Internet of Things (CNIoT 2024), edited by Xiangjie Kong and Cheng Siong Chin. SPIE, 2024. http://dx.doi.org/10.1117/12.3052488.

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Wei, Xinwei, Yiru Lin, Linao Zhang, Zhiyuan Zou, Jianwei Wei, and Wei Yi. "Transformer-based Multi-Target Tracking with Bayesian Perspective." In 2024 27th International Conference on Information Fusion (FUSION). IEEE, 2024. http://dx.doi.org/10.23919/fusion59988.2024.10706292.

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Liu, Xingchi, and Lyudmila Mihaylova. "Active Sensing for Target Tracking: A Bayesian Optimisation Approach." In 2024 27th International Conference on Information Fusion (FUSION). IEEE, 2024. http://dx.doi.org/10.23919/fusion59988.2024.10706282.

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Reed, C. M. "Bayesian track and plot management." In IEE Colloquium. Target Tracking: Algorithms and Applications. IEE, 1999. http://dx.doi.org/10.1049/ic:19990509.

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Tansley, J., D. Lewis, T. Worrall, and P. Thomas. "Bayesian methods for NBC defence." In IEE Colloquium on Target Tracking and Data Fusion. IEE, 1998. http://dx.doi.org/10.1049/ic:19980425.

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Gordon, N. "Bayesian target selection after group pattern distortion." In IEE Colloquium on Target Tracking and Data Fusion. IEE, 1996. http://dx.doi.org/10.1049/ic:19961351.

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Theobald, R. "A Bayesian algorithm to address the radar/ESM track association problem." In Target Tracking 2004: Algorithms and Applications. IEE, 2004. http://dx.doi.org/10.1049/ic:20040058.

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Black, J. V. "A hybrid parametric, non-parametric approach to Bayesian target tracking." In IEE Colloquium on Target Tracking and Data Fusion. IEE, 1996. http://dx.doi.org/10.1049/ic:19961352.

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Kulmon, Pavel. "Bayesian Deghosting Algorithm for Multiple Target Tracking." In 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). IEEE, 2020. http://dx.doi.org/10.1109/mfi49285.2020.9235215.

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Kim, Kwang H. "Bayesian inference network: applications to target tracking." In Aerospace Sensing, edited by Oliver E. Drummond. SPIE, 1992. http://dx.doi.org/10.1117/12.139384.

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