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A hybrid coordinates federated filtering fusion algorithm with information feedback is developed for a passive maneuvering target tracking. The algorithm is designed to deal with angle-only measurements for accurately estimating the kinematic state components of target motion. The hierarchical architecture of the algorithm comprises a group of local processors and a global processor. In each local...
In this paper we provide an intelligent and convenient shopping cart, Intelligent Shopping Assistant System (ISAS). With ISAS, customers can concentrate on what he/she has to buy, and do not be bothered where he/she has to buy or it is want to buy. In contrast to conventional shopping cart, two modes of autonomous functions are added to ISAS in order to reduce the labor of customers for pushing goods...
The article focuses on the research of multiple object tracking of the pedestrian in surveillance video sequence. We apply the tracking-by-detection algorithm framework to tracking, and the algorithm kernel is formulating multiple object tracking as an energy minimization problem. The main research emphasis is the construction of energy function which is expected to represent the real-world scenes...
We formulate multiple object tracking (MOT) as a Generalized Maximum Multi Clique problem (GMMCP) without any simplified version in problem formulation or optimization, which makes full use of image evidence. Afterwards, we solve the GMMCP through Mixed Binary-Integer Program (MBIP) and obtain the final motion trajectories of the multiple objects. In order to improve the algorithm's performance, we...
Multi-camera sensor is used widely in military, industry and disaster rescue and its performance is up to single fisheye camera. Aim to the serious distortion of fisheye camera image, an improved target tracking algorithm is proposed. First, searching window based on sphere coordinates model is set up. Then, color histograms are modulated correspondingly. Finally, particle filter target tracking algorithm...
Background construction is the base of object detection and tracking for the machine vision system. Traditional background modeling methods often require complicated computations and are sensitive to illumination changes and shadow interference. In this paper, we propose a block-based background modeling method, which fully utilizes the color and texture characteristics of each incoming frame. The...
The aim of this paper is to present a federated adaptive filter for use of multi-sensor data fusion systems for target tracking. The computational architecture of the filter consists of several local processors and a global processor. Each local processor incorporates a scheme of Bayesian decision theory into the multiple-model filter to develop a switching capability to react against the same target...
An adaptive filtering approach is present for fusing the tracks of multi-sensor surveillance systems. The approach is an algorithm of hierarchical estimation fusion which consists of several local nodes and a global node. A linear Kalman filter is employed by each local node to produce the track estimate of the same target. The outputs of all local nodes are transmitted to the global node. In this...
A Multiple-Model Adaptive Filter (MMAF) is developed for use in multi-sensor track fusion systems for target tracking. The architecture of hierarchical fusion consists of several local processors and a global processor. Each local processor collects measurement data from a sensor and then using Kalman filter performs tracking function. The global processor utilizes the MMAF which consists of Information...
An estimation fusion algorithm based on a group of hybrid coordinate (HC) filters is presented to the position and velocity estimation using angle-only measurements extracted from multiple maneuverable aircrafts with onboard passive sensor. The algorithm is a hierarchical architecture which consists of several local processors and a global processor. In each local processor, an extended Kalman (EK)...
The focus of the paper is to present the nonlinear estimation fusion in distributed passive sensor networks which include multiple maneuverable aircrafts with onboard direction finder in each one to execute surveillance over the certain area. The main issue addressed in this research is to construct the hierarchical architecture which consists of passive sensors, local processors, and global processor...
An adaptive filter is developed for use in multi-sensor surveillance systems for target tracking. The hierarchical architecture consists of local processors and global processor for distributed fusion. A linear Kalman filter is employed in each local processor to track the same target which is described in the reference Cartesian coordinate system with the radar measuring range, bearing and elevation...
A federated dual-band filter is developed for use in multi-sensor systems for target tracking. Filter architecture consists of local processors and global processor to describe the distributed fusion problem due to correlation across track estimates for the same target. Each local processor incorporates modified probabilistic neural network with multiple model filter (MMF) to develop switching capability...
The focus of this paper is on examining the accuracy of two existing state vector fusion methods, weighted covariance fusion (WCF) and information matrix fusion (IMF), in a multi-sensor environment for computing the fused estimates from distributed Kahnan filters tracking a single maneuvering target. Each sensor tracker utilized in the Reference Cartesian Coordinate System (RCCS) is described for...
The focus of this paper is to present the distributed architecture of track-to-track fusion for computing the fused estimate from multiple filters tracking a maneuvering target with the simplified maximum likelihood estimator. The architecture consists of sensor-based Kalman filters, local processors and global fuser. Each sensor tracker utilized in the reference Cartesian coordinate system is described...
The desired improvements of a tracker rely on more accurate state estimates and less computation loads. A state-vector multisensor data fusion approach that consists of local processor and global processor is described for the problem of tracking a maneuvering target in the local inertial cartesian coordinate system (LICCS). For local processor, the sensor-based filtering algorithm utilized in the...
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