In the domain of medicine and healthcare (M&HC), wireless sensor networks (WSNs) plays a more and more important role. Under some special situations, the object of the WSN is moving leading to the problems of target tracking. Since real-time and communication amount is crucial for the WSN target tracking, the performance of target tracking in the WSN is critically depended on real-time and comm...

This paper presents visual cues for object tracking in video sequences using particle filtering. A consistent histogram-based framework is developed for the analysis of colour, edge and texture cues. The visual models for the cues are learnt from the first frame and the tracking can be carried out using one or more of the cues. A method for online estimation of the noise parameters of the visua...

Video based object tracking normally deals with non-stationary image streams that change over time. Robust and real time moving object tracking is considered to be a problematic issue in computer vision. Multiple object tracking has many practical applications in scene analysis for automated surveillance. In this paper, we introduce a specified object tracking based particle filter used in an e...

Visual tracking is required by many vision applications such as human-computer interfaces and human-robot interactions. However, in daily living spaces where such applications are assumed to be used, stable tracking is often difficult because there are many objects which can cause the visual occlusion. While conventional tracking techniques can handle, to some extent, partial and short-term occ...

Traditional particle filter cannot accommodate to the environment of background interferences, illumination variations and occlusions. This paper presents a face tracking method with fusion of color histogram, contour features and grey model based on particle filter. First, it brought in contour features as the main cue of multiple features when tracking the face without stable color histogram....

Pedestrian tracking is a critical problem in the field of computer vision. Particle filters have been proven to be very useful in pedestrian tracking for nonlinear and non-Gaussian estimation problems. However, pedestrian tracking in complex environment is still facing many problems due to changes of pedestrian postures and scale, moving background, mutual occlusion, and presence of pedestrian....

We report a non-iterative localization algorithm that utilizes the scaling of a three-dimensional (3D) image in the axial direction and focuses on evaluating the radial symmetry center of the scaled image to achieve the desired single-particle localization. Using this approach, we analyzed simulated 3D particle images by wide-field microscopy and confocal microscopy respectively, and the 3D tra...

This paper presents new methods for efficient object tracking in video sequences using multiple features and particle filtering. A histogram-based framework is used to describe the features. Histograms are useful because have the property that they allow changes in the object appearance while the histograms remain the same. Particle filtering is used because it is very robust for non-linear and...

The high-dimensional search space involved in markerless full-body articulated human motion tracking from multiple-views video sequences has led to a number of solutions based on metaheuristics, the most recent form of which is Particle Swarm Optimization (PSO). However, the classical PSO suffers from premature convergence and it is trapped easily into local optima, significantly affecting the ...

Quantitative analysis of dynamic processes in living cells by means of fluorescence microscopy imaging requires tracking of hundreds of bright spots in noisy image sequences. Deterministic approaches, which use object detection prior to tracking, perform poorly in the case of noisy image data. We propose an improved, completely automatic tracker, built within a Bayesian probabilistic framework....