Object tracking with multiple cameras is more efficient than tracking with one camera. In this paper, we propose a multiple-camera multiple-object tracking system that can track 3D object locations even when objects are occluded at cameras. Our system tracks objects and fuses data from multiple cameras by using the probability hypothesis density filter. This method avoids data association betwe...

Motivation The functionality of neurons and their role in neuronal networks is tightly connected to the cell morphology. A fundamental problem in many neurobiological studies aiming to unravel this connection is the digital reconstruction of neuronal cell morphology from microscopic image data. Many methods have been developed for this, but they are far from perfect, and better methods are need...

This paper describes the application of a Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter for tracking objects in surveillance video. Clark et al. have proposed a point-based GM-PHD filter designed for track label consistency. However, this cannot be used for track consistency when using rectangles covering an object. The proposed solution modifies this filter to increase tracki...

Multi-target tracking is a common problem with many applications. In most of these the expected number of targets is not known a priori, so that it has to be estimated from the measured data. Poisson point processes (PPPs) are a very useful theoretical model for diverse applications. One of those is multi-target tracking of an unknown number of targets, leading to the intensity filter (iFilter)...

In this paper, we address the problem of multi-target detection and tracking over a network of separately located Doppler-shift measuring sensors. For this challenging problem, we propose to use the probability hypothesis density (PHD) filter and present two implementations of the PHD filter, namely the sequential Monte Carlo PHD (SMC-PHD) and the Gaussian mixture PHD (GM-PHD) filters. Performa...

Random finite set provides a rigorous foundation for optimal Bayes multi-target filtering. The major hurdle faced in Bayes multi-target filtering is the inherent computational intractability. Even the Probability Hypothesis Density (PHD) filter, which propagates only the first moment (or PHD) instead of the full multi-target posterior, still involves multiple integrals with no closed forms. In ...

In this paper, we propose a simplified particle probability hypothesis density (PHD) filter and its hardware implementation for multiple-target tracking (MTT). In the proposed algorithm, the update step of particle PHD filter is simplified and the time-varying number of measurements is arranged in combination series/parallel mode. This may result in fixed hardware architecture and therefore pre...

Multi-target tracking is an important problem in civilian and military applications. This paper investigates distributed multi-target tracking using autonomous sensor networks. Data association, which arises particularly in multi-object scenarios, can be tackled by various solutions. We consider sequential Monte Carlo implementations of the Probability Hypothesis Density (PHD) filter based on r...

This work considers automated multi target tracking of odontocete whistle contours. An adaptation of the Gaussian mixture probability hypothesis density (GM-PHD) filter is described and applied to the acoustic recordings from six odontocete species. From the raw data, spectral peaks are first identified and then the GM-PHD filter is used to simultaneously track the whistles' frequency contours....

We presents a PHD filtering approach to estimate the state of an unknown number of persons in a video sequence. Persons are represented by moving blobs, which are tracked across different frames using a first-order moment approximation to the posterior density. The PHD filter is a good alternative to standard multi-target tracking algorithms, since overrides making explicit associations INTERNA...