Selective Sampling Importance Resampling Particle Filter Tracking With Multibag Subspace Restoration

Jenkins, M.; Barrie, Peter; Buggy, Tom; Morison, Gordon

doi:10.1109/tcyb.2016.2631660

Cited by 15 publications

(5 citation statements)

References 48 publications

(65 reference statements)

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“…To improve the performance of the algorithm, Nguyen et al [15] proposed to use a Monte Carlo algorithm based on the sampling box to achieve a single and fast convergence. Jenkins et al [16] optimised the weight distribution of particles and improved the reliability of weight in order to get better tracking accuracy and effectively reduce the positioning error. The methods were implemented based on Bayesian estimation.…”

Section: Related Workmentioning

confidence: 99%

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Positioning optimisation based on particle quality prediction in wireless sensor networks

et al. 2019

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The particle degradation problem of particle filter (PF) algorithm caused by reduction of particle weights significantly influences the positioning accuracy of target nodes in wireless sensor networks. This study presents a predictor to obtain the particle swarm of high quality by calculating non-linear variations of ranging between particles and flags and modifying the reference distribution function. To this end, probability variations of distances between particles and star flags are calculated and the maximum inclusive distance using the maximum probability of high-quality particle swarm is obtained. The quality of particles is valued by the Euclidean distance between the predicted and real observations, and hereafter particles of high quality are contained in spherical coordinate system using the distance as diameter. The simulation results show that the proposed algorithm is robust and the computational complexity is low. The method can effectively improve the positioning accuracy and reduce the positioning error of target nodes.

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Section: Related Workmentioning

confidence: 99%

“…Jenkins et al . [16] optimised the weight distribution of particles and improved the reliability of weight in order to get better tracking accuracy and effectively reduce the positioning error. The methods were implemented based on Bayesian estimation.…”

Section: Related Workmentioning

confidence: 99%

Positioning optimisation based on particle quality prediction in wireless sensor networks

et al. 2019

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“…Hence, feature based particle filter algorithms [7][8][9] are preferred to other algorithms. Further, it has been reported in the literature that single feature based object models in particle filters such as color feature based particle filters [22][23][24], edge feature based particle filter [25], motion feature based particle filter [26][27][28], appearance feature based particle filter [29,30], entropy feature based particle filter [31,32] could handle one or two typical issues of the scene during tracking and have achieved good tracking accuracy in initial frames.…”

Section: Introductionmentioning

confidence: 99%

Particle Filter Based Video Object Tracking Scheme With Target Remodeling and Reinitialization and its Hardware Implementation Using Raspberry Pi

Panda

Nanda

Pradhan

2023

Preprint

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Video object tracking in real-world scenarios is one of the challenging problems of computer vision. The issue is compounded in the presence of varying illumination conditions, dynamic entities of the background, and bad weather conditions. In this research, a particle filter based new video object tracking scheme is developed with the proposed notions of target remodeling and reinitialization. During the tracking phase, the target is remodeled in each frame to take care of the changing scene dynamics over frames. The target is remodeled by fused feature distributions chosen from the created bank of fused feature distributions having discriminating potential to differentiate the target and the background in a given frame. The fused feature bank is created by fusing two features from the set consisting of Color, LBP, and HOG features. The features are fused probabilistically where the weights are determined based on the discriminating ability of a given feature. In order to achieve high tracking accuracy, the deviation of the tracker is evaluated ineach frame using the notion of time motion history and reinitialization of the tracker position takes place when the deviation is above a preselected threshold. Besides, the proposed algorithm has been implemented successfully on a Raspberry Pi based hardware setup and thus becomes a potential candidate for real time implementation. The proposed scheme is successfully tested on videos from DAVIS 2016, LASIESTA, and CDnet 2014 databases and the tracking accuracy is found to be higher than those of the existing algorithms.

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“…Firouznia et al introduced the chaos theory into the particle filter framework, effectively reducing the number of particles and search space [18]. Jenkins et al introduced the multibag subspace recovery mechanism to solve the problem of updating the appearance model in particle filter tracking and resetting in case of tracking drift, so as to improve the robustness of the model [19]. Although the tracking algorithm based on particle filter framework is constantly improving [20][21][22], there are still some problems in the existing algorithm, such as poor universality in complex scenes, and the tracking accuracy needs to be further improved.…”

Section: Introductionmentioning

confidence: 99%

Smart City Moving Target Tracking Algorithm Based on Quantum Genetic and Particle Filter

Liu

Shang

Hua

2020

Wireless Communications and Mobile Computing

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In the application of moving target tracking in smart city, particle filter technology has the advantages of dealing with nonlinear and non-Gaussian problems, but when the standard particle filter uses resampling method to solve the degradation phenomenon, simply copying the particles will cause local optimization difficulties, resulting in unstable filtering accuracy. In this paper, a particle filter algorithm combined with quantum genetic algorithm (QGA) is proposed to solve the above problems. Aiming at the problem of particle exhaustion in particle filter, the algorithm adopts the method of combining evolutionary algorithm. Each particle in particle filter is regarded as a chromosome in genetic algorithm, and the fitness of each chromosome corresponds to the weight of particle. For each particle state with weight, the particle is first binary coded with qubit and quantum superposition state, and then quantum rotation gate is used for selection, crossing, mutation, and other operations, after a set number of iterations, the final particle set with accuracy and better diversity. In this paper, the filter state estimation and RMSF of N=50 and N=100 for nonlinear target tracking and the comparison of real state and state estimation trajectory in time-constant model under nonlinear target tracking are given. It can be seen that in nonlinear state, the quantum genetic and particle filter (QGPF) algorithm can achieve a higher accuracy of state estimation, and the filtering error of QGPF algorithm at each time is relatively uniform, which shows that the algorithm in this paper has better algorithm stability. Under the time-constant model, the algorithm fits the real state and realizes stable and accurate tracking.

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Selective Sampling Importance Resampling Particle Filter Tracking With Multibag Subspace Restoration

Cited by 15 publications

References 48 publications

Positioning optimisation based on particle quality prediction in wireless sensor networks

Positioning optimisation based on particle quality prediction in wireless sensor networks

Particle Filter Based Video Object Tracking Scheme With Target Remodeling and Reinitialization and its Hardware Implementation Using Raspberry Pi

Smart City Moving Target Tracking Algorithm Based on Quantum Genetic and Particle Filter

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