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The traditional PHD tracker (PHDT) generates the newborn PHD from the prior knowledge, thus can not be applied when the prior newborn target knowledge is unavailable. To this end, we propose a historical information feedback multiple-target tracker (HIFMTT) as an improvement to the traditional PHDT. The HIFMTT generates the newborn PHD through processing the historical observation and estimating results...
In this present world even though technology has improved tremendously, real time target tracking is still considered to be an important and challenging research area. In target tracking the aim is to estimate the kinematic state of an observed object. Particle filter offers a general solution for such problems, however the main concern is its computational complexity which increases quickly with...
In this paper, we consider the challenging problem of tracking a maneuvering target with abrupt accelerations and introduce a new model based on GARCH process. We formulate the acceleration of dynamic model by stochastic differential equation (SDE) with adaptive coefficients and stochastic volatility. Our adaptive state space approach provides a novel dynamic model that naturally facilitates the physical...
A particle filter target tracking algorithm is proposed to solve tracking problems in complex scene. Used united histogram according to weighted background as in Eq. 7 to describe gradient direction and grayscale features imformation of target, adjusted features weights adaptively in the observational model based on features' dependability as in Eq.9, and combined the observational model with particle...
In the application of particle filter algorithm for target tracking in wireless sensor networks, an Auxiliary particle filter (APF) and Gaussian particle filter (GPF) are discussed to solve the particle degradation problem of Particle Filter (PF). By introducing an auxiliary variable and two rounds weighted processes, APF makes the particle weights stable and relaxes particle degradation. GPF uses...
Top down tracking approaches like particle filtering are known for their robustness since they can handle multimodal probability density functions. Active appearance models (AAMs), on the other hand, allow for precise, model-based tracking but suffer from limited robustness. The particle filter AAM combination (PFAAM), exploits the best of both worlds. In this paper the PFAAM is embedded on a smart...
The problem addressed in this work is to separate the signals of moving sources with independent component analysis (ICA) and tracking the kinematics (position, velocity, acceleration) of each individual source in the working space using particle filters. To identify the unpredictable movement of the speaker over time, the new proposed state switching scheme handles the uncertainty of the speaker's...
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