scholarly journals An Adaptive PHD Filter for Multitarget Tracking with Multispectral Data Fusion

2015 ◽  
Vol 2015 ◽  
pp. 1-11
Author(s):  
Guoliang Zhang ◽  
Chunling Yang ◽  
Yan Zhang
Keyword(s):  
Particle Filtering ◽  
Synthetic Data ◽  
Probability Of Detection ◽  
Multispectral Image ◽  
Fusion Algorithm ◽  
Multiple Targets ◽  
Probability Hypothesis Density ◽  
Multispectral Data ◽  
Detection Model ◽  
Detection And Tracking

In order to improve the detection and tracking performance of multiple targets from IR multispectral image sequences, the approach based on spectral fusion algorithm and adaptive probability hypothesis density (PHD) filter is proposed. Firstly, the nonstationary adaptive suppression method is proposed to remove the background clutter. Based on the multispectral image sequence, the spectral fusion method is used to detect the abnormal targets. Spectral fusion produces the appropriate binary detection model and the computational probability of detection. Secondly, the particle filtering-based adaptive PHD algorithm is developed to detect and track multiple targets. This algorithm can deal with the nonlinear measurement on target state. In addition, the calculated probability of detection substitutes the fixed detection probability in PHD filter. Finally, the synthetic data sets based on various actual background images were utilized to validate the effectiveness of the detection approach. The results demonstrate that the proposed approach outperforms the conventional sequential PHD filtering in terms of detection and tracking performances.

scholarly journals Robust Target Detection and Tracking Algorithm Based on Roadside Radar and Camera

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1116
Author(s):  
Jie Bai ◽  
Sen Li ◽  
Han Zhang ◽  
Libo Huang ◽  
Ping Wang
Keyword(s):  
Target Detection ◽  
Traffic Safety ◽  
Gaussian Mixture ◽  
Transportation Systems ◽  
Fusion Algorithm ◽  
Smart Cameras ◽  
Probability Hypothesis Density ◽  
Detection And Tracking ◽  
Wave Radar ◽  
Perception System

Intelligent transportation systems (ITSs) play an increasingly important role in traffic management and traffic safety. Smart cameras are the most widely used sensors in ITSs. However, cameras suffer from a reduction in detection and positioning accuracy due to target occlusion and external environmental interference, which has become a bottleneck restricting ITS development. This work designs a stable perception system based on a millimeter-wave radar and camera to address these problems. Radar has better ranging accuracy and weather robustness, which is a better complement to camera perception. Based on an improved Gaussian mixture probability hypothesis density (GM-PHD) filter, we also propose an optimal attribute fusion algorithm for target detection and tracking. The algorithm selects the sensors’ optimal measurement attributes to improve the localization accuracy while introducing an adaptive attenuation function and loss tags to ensure the continuity of the target trajectory. The verification experiments of the algorithm and the perception system demonstrate that our scheme can steadily output the classification and high-precision localization information of the target. The proposed framework could guide the design of safer and more efficient ITSs with low costs.

scholarly journals Coupled hydrogeophysical parameter estimation using a sequential Bayesian approach

2010 ◽  
Vol 14 (3) ◽  
pp. 545-556 ◽  
Author(s):  
J. Rings ◽  
J. A. Huisman ◽  
H. Vereecken
Keyword(s):  
Water Content ◽  
Particle Filtering ◽  
Field Data ◽  
Synthetic Data ◽  
Time Domain Reflectometry ◽  
Second Step ◽  
Parameter Estimates ◽  
Posterior Distributions ◽  
Data Set ◽  
Resistivity Tomography

Abstract. Coupled hydrogeophysical methods infer hydrological and petrophysical parameters directly from geophysical measurements. Widespread methods do not explicitly recognize uncertainty in parameter estimates. Therefore, we apply a sequential Bayesian framework that provides updates of state, parameters and their uncertainty whenever measurements become available. We have coupled a hydrological and an electrical resistivity tomography (ERT) forward code in a particle filtering framework. First, we analyze a synthetic data set of lysimeter infiltration monitored with ERT. In a second step, we apply the approach to field data measured during an infiltration event on a full-scale dike model. For the synthetic data, the water content distribution and the hydraulic conductivity are accurately estimated after a few time steps. For the field data, hydraulic parameters are successfully estimated from water content measurements made with spatial time domain reflectometry and ERT, and the development of their posterior distributions is shown.

scholarly journals A probabilistic method for structural integrity assurance based on damage detection structural health monitoring data

2021 ◽  
pp. 147592172110388
Author(s):  
Michael Siu Hey Leung ◽  
Joseph Corcoran
Keyword(s):  
Damage Detection ◽  
Particle Filtering ◽  
Structural Integrity ◽  
Probabilistic Method ◽  
Probability Of Detection ◽  
Monitoring Systems ◽  
Negative Results ◽  
Systematic Effects ◽  
Detection Capabilities ◽  
Integrity Assurance

The value of using permanently installed monitoring systems for managing the life of an engineering asset is determined by the confidence in its damage detection capabilities. A framework is proposed that integrates detection data from permanently installed monitoring systems with probabilistic structural integrity assessments. Probability of detection (POD) curves are used in combination with particle filtering methods to recursively update a distribution of postulated defect size given a series of negative results (i.e. no defects detected). The negative monitoring results continuously filter out possible cases of severe damage, which in turn updates the estimated probability of failure. An implementation of the particle filtering method that takes into account the effect of systematic uncertainty in the detection capabilities of a monitoring system is also proposed, addressing the problem of whether negative measurements are simply a consequence of defects occurring outside the sensors field of view. A simulated example of fatigue crack growth is used to demonstrate the proposed framework. The results demonstrate that permanently installed sensors with low susceptibility to systematic effects may be used to maintain confidence in fitness-for-service while relying on fewer inspections. The framework provides a method for using permanently installed sensors to achieve continuous assessments of fitness-for-service for improved integrity management.

Visual Tracking Using Multimodal Particle Filter

2018 ◽  
pp. 1072-1090 ◽  
Author(s):  
Tony Tung ◽  
Takashi Matsuyama
Keyword(s):  
Visual Tracking ◽  
Particle Filtering ◽  
Body Parts ◽  
Detection And Tracking ◽  
Flow Features ◽  
Target Templates ◽  
Textured Models ◽  
Online Mechanism ◽  
Changes Over Time ◽  
Appearance Changes

Visual tracking of humans or objects in motion is a challenging problem when observed data undergo appearance changes (e.g., due to illumination variations, occlusion, cluttered background, etc.). Moreover, tracking systems are usually initialized with predefined target templates, or trained beforehand using known datasets. Hence, they are not always efficient to detect and track objects whose appearance changes over time. In this paper, we propose a multimodal framework based on particle filtering for visual tracking of objects under challenging conditions (e.g., tracking various human body parts from multiple views). Particularly, the authors integrate various cues such as color, motion and depth in a global formulation. The Earth Mover distance is used to compare color models in a global fashion, and constraints on motion flow features prevent common drifting effects due to error propagation. In addition, the model features an online mechanism that adaptively updates a subspace of multimodal templates to cope with appearance changes. Furthermore, the proposed model is integrated in a practical detection and tracking process, and multiple instances can run in real-time. Experimental results are obtained on challenging real-world videos with poorly textured models and arbitrary non-linear motions.

Probability of detection model for fatigue crack in ultrasonic infrared imaging

2016 ◽  
Vol 45 (3) ◽  
pp. 0304005
Author(s):  
冯辅周 Feng Fuzhou ◽  
张超省 Zhang Chaosheng ◽  
宋爱斌 Song Aibin ◽  
闵庆旭 Min Qingxu ◽  
朱俊臻 Zhu Junzhen
Keyword(s):  
Fatigue Crack ◽  
Infrared Imaging ◽  
Probability Of Detection ◽  
Detection Model

scholarly journals A Grey Wolf Optimization-based Track-Before-Detect Method for Maneuvering Extended Target Detection and Tracking

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1577 ◽  
Author(s):  
Bo Yan ◽  
Xu Yang Zhao ◽  
Na Xu ◽  
Yu Chen ◽  
Wen Bo Zhao
Keyword(s):  
Target Detection ◽  
Synthetic Data ◽  
Optimal Solution ◽  
Real Data ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Detection And Tracking ◽  
Extended Target ◽  
Air Surveillance ◽  
Track Before Detect

A grey wolf optimization-based track-before-detect (GWO-TBD) method is developed for extended target detection and tracking. The aim of the GWO-TBD is tracking weak and maneuvering extended targets in a cluttered environment using the measurement points of an air surveillance radar. The optimal solution is the trajectory constituted by the points of an extended target. At the beginning of the GWO-TBD, the measurements of each scan are clustered into alternative sets. Secondly, closely sets are associated for tracklets. Each tracklet equals a candidate solution. Thirdly, the tracklets are further associated iteratively to find a better solution. An improved GWO algorithm is developed in the iteration for removal of unappreciated solution and acceleration of convergence. After the iteration of several generations, the optimal solution can be achieved, i.e. trajectory of an extended target. Both the real data and synthetic data are performed with the GWO-TBD and several existing algorithms in this work. Result infers that the GWO-TBD is superior to the others in detecting and tracking maneuvering targets. Meanwhile, much less prior information is necessary in the GWO-TBD. It makes the approach is engineering friendly.

Model for the detection and tracking of multiple targets

1995 ◽  
Author(s):  
James A. Anderson ◽  
N. C. Mohanty ◽  
Pradeep K. Bhattacharya ◽  
A. S. King
Keyword(s):  
Multiple Targets ◽  
Detection And Tracking

scholarly journals Nudging the particle filter

2019 ◽  
Vol 30 (2) ◽  
pp. 305-330 ◽  
Author(s):  
Ömer Deniz Akyildiz ◽  
Joaquín Míguez
Keyword(s):  
Particle Filtering ◽  
Particle Filters ◽  
Synthetic Data ◽  
Alternative Interpretation ◽  
Error Rates ◽  
Particle Methods ◽  
Model Errors ◽  
Actual System ◽  
Model Dynamics ◽  
Nonlinear Tracking

Abstract We investigate a new sampling scheme aimed at improving the performance of particle filters whenever (a) there is a significant mismatch between the assumed model dynamics and the actual system, or (b) the posterior probability tends to concentrate in relatively small regions of the state space. The proposed scheme pushes some particles toward specific regions where the likelihood is expected to be high, an operation known as nudging in the geophysics literature. We reinterpret nudging in a form applicable to any particle filtering scheme, as it does not involve any changes in the rest of the algorithm. Since the particles are modified, but the importance weights do not account for this modification, the use of nudging leads to additional bias in the resulting estimators. However, we prove analytically that nudged particle filters can still attain asymptotic convergence with the same error rates as conventional particle methods. Simple analysis also yields an alternative interpretation of the nudging operation that explains its robustness to model errors. Finally, we show numerical results that illustrate the improvements that can be attained using the proposed scheme. In particular, we present nonlinear tracking examples with synthetic data and a model inference example using real-world financial data.

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