Optical sensors based vehicle tracking can be widely implemented in traffic surveillance and flow control. The vast development of video surveillance infrastructure in recent years has drawn the current research focus towards vehicle tracking using high-end and low cost optical sensors. However, tracking vehicles via such sensors could be challenging due to the high probability of changing vehi...

The random set approach to multitarget tracking is a theoretically sound framework that covers joint estimation of the number of targets and the state of the targets. This paper describes a particle filter implementation of the random set multitarget filter. The contribution of this paper to the random set tracking framework is the formulation of a measurement model where each sensor report is ...

In this article, we propose a new particle filtering scheme, called a switching particle filter, which allows robust and accurate visual tracking under typical circumstances of real-time visual tracking. This scheme switches two complementary sampling algorithms, Condensation and Auxiliary Particle Filter, in an on-line fashion based on the confidence of the filtered state of the visual target....

Aimed to problem that particles exist degradation in particle filter (PF) algorithm for target tracking which is used in Wireless Sensor Networks, a new particle filter target tracking algorithm (GPF) based on genetic algorithm was proposed in this paper. First, based on the description of the PF algorithm, the factors that affect the degradation were analyzed, which were importance function an...

Connection tracking subsystem on Linux tracks states of network flows. It is utilized by packet filter for stateful filtering. In this paper, we propose solutions to issues that arise where connection tracking subsystem handles Routing Header and Mobile IPv6. We also describe how the current connection tracking subsystem handles IPv6 fragments without disturbing Path MTU discovery. As a result ...

Particle filter algorithms are widely used for object tracking in video sequences, but the standard particle filter algorithm cannot solve the validity of particles ideally. To solve the problems of particle degeneration and sample impoverishment in a particle filter tracking algorithm, an improved object tracking algorithm is proposed, which combines ...

This paper presents a method for multi camera image tracking in the context of image surveillance. The approach differs from most methods in that we exploit multiple camera views to resolve object occlusion. Moving objects are detected by using background subtraction. Viewpoint correspondence between the detected objects is then established by using the ground plane homography constraint. The K...

Aiming at the multi-source heterogeneous of target tracking system information. On the basis of "current" statistical model, this paper researches the unscented Kalman filter information fusion method and analyzes its mathematical model. According to the mathematic model researching of optimal estimation real time tracking algorithm, it be able to describe and process the sensor information of ...

— This paper presents review of techniques and algorithms used for filtering and data association in visual tracking. Kalman filter is an optimal Bayesian filter for linear dynamic models with Gaussian noise. Most of the processes and systems in real world are nonlinear, and in these situations there is extension of Kalman filter named the Extended Kalman filter (EKF). In case when the noise is...

Particle filtering has been successfully used in target tracking applications when the state and measurement models are nonlinear and the associated noise is non-Gaussian. Among the most difficult scenarios of target tracking, are multiple target tracking (MTT) problems that require the application of realistic and often multiple kinematic models. Recently algorithms such as the independent par...