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Stochastic filters have been extensively used for object tracking because of its ability to measure uncertainties and high accuracy. In recent years, the availability of cheap computers with high computational power has led to incorporate tracking systems in many consumer electronics devices such as surveillance cameras and game consoles. In this paper, we compare Kalman filter and particle filter...
In the recent years, 3D tracking has gained attention due to the perforation of powerful computers and the increasing interest in tracking applications. One of the most common tracking algorithms used is the Kalman filter. Kalman filter is a linear estimator that is based on approximating system's dynamics using Gaussian probability distribution. In this paper, we provide a detailed evaluation of...
In this paper, we propose a vehicle detection and tracking algorithm. The detection is done using the median filtering and blob extraction. Median filtering is used for background extraction which is later subtracted from the motion frames for object detection. Morphological operators are employed for blob extraction. Hence, object detection is achieved using median filtering and morphological closing...
A linear dynamical model can be used to describe the evolution of an unknown system in noisy conditions. However, in most applications model parameters of a dynamical system are not known a priori, bringing into question the optimality of traditional state-only estimators. In this paper, we consider block-frequency-domain dynamical models and formulate an optimal framework for low-latency joint state...
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