Object detection and tracking have been studied separately in most cases. Particle filtering has proven very successful for non-linear and nonGaussian estimation problems. This paper presents a new method for tracking moving vehicles with temporal disappearance. The proposed method can continue tracking after disappearance. Color distribution of objects is integrated into particle filtering alg...

A recently introduced particle filtering method, called LS-N-IPS, is considered for tracking objects on video sequences. LS-N-IPS is a computationally efficient particle filter that performs better than the standard N-IPS particle filter, when observations are highly peaky, as it is the case of visual object tracking problems with good observation models. An implementation of LS-N-IPS that uses...

In this paper, we present a tracking‐by‐multiple hypotheses framework to detect and track multiple vehicles accurately and precisely. The tracking‐by‐ multiple hypotheses framework consists of obstacle detection, vehicle recognition, visual tracking, global position tracking, data association and particle filtering. The multiple hypotheses are from obstacle detect...

Successful multi-target tracking requires locating the targets and labeling their identities. For the multi-target tracking systems, the latter becomes more challenging when the targets frequently interact with each other. In this paper, we propose a method for multiple persons tracking using multiple cameras and floor sensors. Our method estimates 3D positions of human body and head, and label...

This paper studies the problem of tracking a mobile device in mixed line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. NLOS error is assumed to be Gaussian with unknown mean and variance. An adaptive Rao-Blackwellized particle filter (RBPF) is proposed for mobile tracking in such scenarios. An extended Kalman filter is used to approximately estimate the mobile state, and the particle ...

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...

A new data analysis tool that resolves correlations on the nanometer length and millisecond timescale is derived. This tool, adapted from methods of spatiotemporal image correlation spectroscopy, exploits the high positional accuracy of single-particle tracking. While conventional tracking methods break down if multiple particle trajectories intersect, our method works in principle for arbitrar...

In this paper, we propose an efficient hybrid Particle Filter (PF) algorithm for video tracking by employing a genetic algorithm to solve the sample impoverishment problem. In the presented method, the object to be tracked is selected by a rectangular window inside which a few numbers of particles are scattered. The particles’ weights are calculated based on the similarity between feature vecto...

Tracking deforming objects involves estimating the global motion of the object and its local deformations as functions of time. Tracking algorithms using Kalman filters or particle filters have been proposed for tracking such objects, but these have limitations due to the lack of dynamic shape information. In this paper, we propose a novel method based on employing a locally linear embedding in...