Target Tracking Based on Adaptive Particle Filter
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This paper presents a method that can track non-rigid moving objects using adaptive particle filter based on spatiograms. Particle filters offer a probabilistic framework for dynamic state estimation and have proven to work well in target tracking. Two key components of particle filters are observation models and motion models. Firstly, because the observation model based on general color histograms discards the spatial information of images, the accuracy of the observation model is decreased. We adopt a proper observation model based on spatiograms which are histograms augmented with spatial means and covariances to capture a richer description of targets and increase robustness in tracking. Secondly, approximate fixed motion models used in practice, such as unrestricted random walking model with fixed noise variance, are not accurate enough. To overcome this problem, we adopt the adaptive multivariate autoregressive models which are computed via the regression analysis. The proposed adaptive motion models can adjust the model order, process noise variance and model parameters automatically. Also, the number of particles is adjusted automatically. The experiments show that the proposed algorithm can effectively track moving objects and increase the robustness in tracking. Its performance is compared with that of the general particle filtering algorithm to demonstrate the advantages of the new method.Keywords:
Robustness
State-space representation
Auxiliary particle filter
Particle (ecology)
Alpha beta filter
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In this paper we propose a method for determining the number of regimes in threshold autoregressive models using smooth transition autoregression as a tool. As the smooth transition model is just an approximation to the threshold autoregressive one, no asymptotic properties are claimed for the proposed method. Tests available for testing the adequacy of a smooth transition autoregressive model are applied sequentially to determine the number of regimes. A simulation study is performed in order to find out the finite-sample properties of the procedure and to compare it with two other procedures available in the literature. We find that our method works reasonably well for both single and multiple threshold models.
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Threshold model
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A statistical inference for random coefficient first-order autoregressive model $[RCAR(1)]$ was investigated by P.M. ROBINSON (1978) in which the coefficients varying over individuals. In this paper we attempt to generalize this result to random coefficient autoregressive model of order $p$ $[RCAR(p)]$. The stationarity condition will derived for this model.
Statistical Inference
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State-space representation
Auxiliary particle filter
Particle (ecology)
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Future frame prediction has been approached through two primary methods: autoregressive and non-autoregressive. Autoregressive methods rely on the Markov assumption and can achieve high accuracy in the early stages of prediction when errors are not yet accumulated. However, their performance tends to decline as the number of time steps increases. In contrast, non-autoregressive methods can achieve relatively high performance but lack correlation between predictions for each time step. In this paper, we propose an Implicit Stacked Autoregressive Model for Video Prediction (IAM4VP), which is an implicit video prediction model that applies a stacked autoregressive method. Like non-autoregressive methods, stacked autoregressive methods use the same observed frame to estimate all future frames. However, they use their own predictions as input, similar to autoregressive methods. As the number of time steps increases, predictions are sequentially stacked in the queue. To evaluate the effectiveness of IAM4VP, we conducted experiments on three common future frame prediction benchmark datasets and weather\&climate prediction benchmark datasets. The results demonstrate that our proposed model achieves state-of-the-art performance.
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The autoregressive process has been used by several authors to model MPEG video traffic and attempts to capture the frame correlation as well as the Gaussian shape of the bit rate variation. However, the autoregressive process alone does not capture scene changes. In this paper, we propose an autoregressive model of order P, AR(P) + IAP (interrupted autoregressive process), to capture scene changes. We compare the model performance to that of the actual video trace, as well as the autoregressive process without scene changes.
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The application of neural networks to non-linear dynamic system identification tasks has a long history, which consists mostly of autoregressive approaches. Autoregression, the usage of the model outputs of previous time steps, is a method of transferring a system state between time steps, which is not necessary for modeling dynamic systems with modern neural network structures, such as gated recurrent units (GRUs) and Temporal Convolutional Networks (TCNs). We compare the accuracy and execution performance of autoregressive and non-autoregressive implementations of a GRU and TCN on the simulation task of three publicly available system identification benchmarks. Our results show, that the non-autoregressive neural networks are significantly faster and at least as accurate as their autoregressive counterparts. Comparisons with other state-of-the-art black-box system identification methods show, that our implementation of the non-autoregressive GRU is the best performing neural network-based system identification method, and in the benchmarks without extrapolation, the best performing black-box method.
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Identification
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This work assesses the forecasts of three nonlinear methods | Markov Switching Autoregressive Model, Logistic Smooth Transition Auto-regressive Model, and Auto-metrics with Dummy Saturation | for the Brazilian monthly industrial production and tests if they are more accurate than those of naive predictors such as the autoregressive model of order p and the double di erencing device. The results show that the step dummy saturation and the logistic smooth transition autoregressive can be superior to the double di erencing device, but the linear autoregressive model is more accurate than all the other methods analyzed.
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Saturation (graph theory)
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