The likelihood models used in probabilistic visual tracking applications are often complex non-linear and/or nonGaussian functions, leading to analytically intractable inference. Solutions then require numerical approximation techniques, of which the particle filter is a popular choice. Particle filters, however, degrade in performance as the dimensionality of the state space increases and the ...

A multitude of cellular and subcellular processes depend critically on the mechanical deformability of the cytoplasm. We have recently introduced the method of particle-tracking microrheology, which measures the viscoelastic properties of the cytoplasm locally and with high spatiotemporal resolution. Here we establish the basic principles of particle-tracking microrheology, describing the advan...

Robust real−time tracking of non−rigid objects in a dynamic environment is a challenging task. Among various cues in tracking, color can provide an efficient visual cue for this type of tracking problem because of its invariance in the presence of changing complex shapes and appearances. To track the color object, a particle filter uses several hypotheses simultaneously and weights them by thei...

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...

Considering that the robust real-time tracking of non-rigid objects is difficult to realize, We present an objects detecting and tracking method based on mean-shift particle filter (MSPF) and support vector machine (SVM). The proposed algorithm uses the mean-shift vector of the tracking object to update the state transition matrix of particle filter algorithm, and we define the criterion of the...

Modern developments in time-lapse fluorescence microscopy enable the observation of a variety of processes exhibited by viruses. The dynamic nature of these processes requires the tracking of viruses over time to explore spatial-temporal relationships. In this work, we developed deterministic and probabilistic approaches for multiple virus tracking in multi-channel fluorescence microscopy image...

We address the problem of visual tracking of arbitrary objects that undergo significant scale and appearance changes. The classical tracking methods rely on the bounding box surrounding the target object. Regardless of the tracking approach, the use of bounding box quite often introduces background information. This information propagates in time and its accumulation quite often results in drif...