We propose a novel approach for head tracking, which combines particle filters with Isomap. The particle filter works on the low-dimensional embedding of training images. It indexes into the Isomap with its state variables to find the closest template for each particle. The most weighted particle approximates the location of head. We develop a synthetic video sequence to test our technique. The results we get show that the tracker tracks the head which changes position, poses and lighting conditions.
標簽： approach combines particle tracking
上傳時間： 2016-01-02
上傳用戶：yy541071797
In this paper, we consider the problem of filtering in relational hidden Markov models. We present a compact representation for such models and an associated logical particle filtering algorithm. Each particle contains a logical formula that describes a set of states. The algorithm updates the formulae as new observations are received. Since a single particle tracks many states, this filter can be more accurate than a traditional particle filter in high dimensional state spaces, as we demonstrate in experiments.
標簽： relational filtering consider problem
上傳時間： 2016-01-02
上傳用戶：海陸空653
Rao-Blackwellised Particle Filters (RBPFs) are a class of Particle Filters (PFs) that exploit conditional dependencies between parts of the state to estimate. By doing so, RBPFs can improve the estimation quality while also reducing the overall computational load in comparison to original PFs. However, the computational complexity is still too high for many real-time applications. In this paper, we propose a modified RBPF that requires a single Kalman Filter (KF) iteration per input sample. Comparative experiments show that while good convergence can still be obtained, computational efficiency is always drastically increased, making this algorithm an option to consider for real-time implementations.
標簽： Particle Filters Rao-Blackwellised exploit
上傳時間： 2016-01-02
上傳用戶：refent
The need for accurate monitoring and analysis of sequential data arises in many scientic, industrial and nancial problems. Although the Kalman lter is effective in the linear-Gaussian case, new methods of dealing with sequential data are required with non-standard models. Recently, there has been renewed interest in simulation-based techniques. The basic idea behind these techniques is that the current state of knowledge is encapsulated in a representative sample from the appropriate posterior distribution. As time goes on, the sample evolves and adapts recursively in accordance with newly acquired data. We give a critical review of recent developments, by reference to oil well monitoring, ion channel monitoring and tracking problems, and propose some alternative algorithms that avoid the weaknesses of the current methods.
標簽： monitoring sequential industria accurate
上傳時間： 2013-12-17
上傳用戶：familiarsmile
To estimate the input-output mapping with inputs x ％ and outputs y generated by the following nonlinear, ％ nonstationary state space model: ％ x(t+1) = 0.5x(t) + [25x(t)]/[(1+x(t))^(2)] ％ + 8cos(1.2t) + process noise ％ y(t) = x(t)^(2) / 20 + 6 squareWave(0.05(t-1)) + 3 ％ + time varying measurement noise ％ using a multi-layer perceptron (MLP) and both the EKF and ％ the hybrid importance-samping resampling (SIR) algorithm.
標簽： input-output the generated following
上傳時間： 2014-01-05
上傳用戶：royzhangsz
2． Using Gaussian elimination method and Gaussian elimination method with row scaled method to solve the following tri-diagonal system for n=10 and 100
標簽： method elimination Gaussian scaled
上傳時間： 2013-12-31
上傳用戶：lyy1234
State_space_reconstruction_parameters_in_the_analysis_of_chaotic_time_series_-_the_role_of_the_time_window_length. It is used for reconstruction of state space in chaotic time series, and also how to determine time window.
標簽： State_space_reconstruction_parame ters_in_the_analysis_of_chaotic_t the_role_of_
上傳時間： 2013-12-21
上傳用戶：fandeshun
Nonlinear_dynamics_delay_times_and_embedding_windows. How to determine embedded window for chaotic state space of time series
標簽： Nonlinear_dynamics_delay_times_an d_embedding_windows determine embedded
上傳時間： 2016-02-21
上傳用戶：tianyi223
一個遺傳算法 這是一個非常簡單的遺傳算法源代碼，是由Denis Cormier (North Carolina State University)開發的，Sita S.Raghavan (University of North Carolina at Charlotte)修正。代碼保證盡可能少，實際上也不必查錯。對一特定的應用修正此代碼，用戶只需改變常數的定義并且定義“評價函數”即可。注意代碼 的設計是求最大值，其中的目標函數只能取正值；且函數值和個體的適應值之間沒有區別。該系統使用比率選擇、精華模型、單點雜交和均勻變異。如果用 Gaussian變異替換均勻變異，可能得到更好的效果。代碼沒有任何圖形，甚至也沒有屏幕輸出，主要是保證在平臺之間的高可移植性。讀者可以從ftp.uncc.edu, 目錄 coe/evol中的文件prog.c中獲得。要求輸入的文件應該命名為‘gadata.txt’；系統產生的輸出文件為‘galog.txt’。輸入的 文件由幾行組成：數目對應于變量數。且每一行提供次序——對應于變量的上下界。如第一行為第一個變量提供上下界，第二行為第二個變量提供上下界，等等。
上傳時間： 2013-12-20
上傳用戶：myworkpost
The software implements particle filtering and Rao Blackwellised particle filtering for conditionally Gaussian Models. The RB algorithm can be interpreted as an efficient stochastic mixture of Kalman filters. The software also includes efficient state-of-the-art resampling routines. These are generic and suitable for any application. For details, please refer to Rao-Blackwellised Particle Filtering for Fault Diagnosis and On Sequential Simulation-Based Methods for Bayesian Filtering After downloading the file, type "tar -xf demo_rbpf_gauss.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab and run the demo.
標簽： filtering particle Blackwellised conditionall
上傳時間： 2014-12-05
上傳用戶：410805624
