Multi-target tracking method based on improved firefly algorithm optimized particle filter

Tian, Mengmeng; Bo, Yuming; Chen, Zhimin; Wu, Panlong; Cong, Yang

doi:10.1016/j.neucom.2019.06.003

Cited by 32 publications

(15 citation statements)

References 22 publications

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“…The proposed approach has been verified over several constrained optimization problems and discrete optimization tasks, which demonstrated its superiority over other optimization algorithms in the literature. An improved FA was proposed to optimize the multi-target tracking method based on particle filter [51]. The experimental results indicate the effectiveness and tracking accuracy of the developed method.…”

Section: Introductionmentioning

confidence: 97%

Predicting Green Consumption Behaviors of Students Using Efficient Firefly Grey Wolf-Assisted K-Nearest Neighbor Classifiers

Tang

Lin

et al. 2020

IEEE Access

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Understanding the green consumption behaviors of college students is highly demanded to update the public and educational policies of universities. For this purpose, this research is devoted to advance an efficient model for identifying prominent features and predicting the green consumption behaviors of college students. The proposed prediction model is based on the K-Nearest Neighbor (KNN) with an effective swarm intelligence method, which is called OBLFA_GWO. The optimization core takes advantage of the firefly algorithm (FA) and opposition-based learning (OBL) to mitigate the immature convergence of the grey wolf algorithm (GWO). In the proposed prediction framework, OBLFA_GWO is utilized to identify influential features. Then, the enhanced KNN model is used to identify the importance and interrelationships of features in samples and construct an effective and stable predictive model for decision support. Five other wellknown algorithms are employed to validate the effectiveness of the proposed OBLFA_GWO strategy using 13 benchmark test problems. Also, the non-parametric statistical Wilcoxon sign rank and Friedman tests are conducted to validate the significance of the proposed OBLFA_GWO against other peers. Experimental results indicate that the FA and OBL can significantly boost the core exploratory and exploitative trends of GWO in dealing with the optimization tasks. Also, the OBLFA_GWO-based KNN (OBLFA_GWO-KNN) model is compared with four classical classifiers, such as kernel extreme learning machine (KELM), backpropagation neural network method (BPNN), and random forest (RF) and five advanced feature selection methods in terms of four standard evaluation indexes. The experimental results show that the classification accuracy of the proposed OBLFA_GWO-KNN can reach to 96.334 % on the real-life dataset collected from nine universities. Also, the proposed binary OBLFA_GWO algorithm has improved the classification performance of KNN compared to the other peers. Hopefully, the established adaptive OBLFA_GWO-KNN model can be considered as a useful tool for predicting students' behavior of green consumption. INDEX TERMS K-nearest neighbor, firefly algorithm, grey wolf algorithm, opposition-based learning, green consumption behavior, feature selection. The associate editor coordinating the review of this manuscript and approving it for publication was Josue Antonio Nescolarde Selva .

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Section: Introductionmentioning

confidence: 97%

Predicting Green Consumption Behaviors of Students Using Efficient Firefly Grey Wolf-Assisted K-Nearest Neighbor Classifiers

Tang

Lin

et al. 2020

IEEE Access

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“…The model presented here uses a particle filter algorithm [1] to increase the robustness to false detections and noise-corrupted measurements. In recent times, there is a growing popularity of particle filters (PFs) in signal processing and communication applications to solve various state estimation problems like tracking [2], localization, navigation [3], and fault diagnosis [4]. PFs have been applied for models described using a dynamic state-space approach comprising a system model representing the state evolution and a measurement model representing the noisy measurements of the state [5].…”

Section: Introductionmentioning

confidence: 99%

FPGA Implementation of Particle Filters for Robotic Source Localization

2021

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Particle filtering is very reliable in modelling non-Gaussian and non-linear elements of physical systems, which makes it ideal for tracking and localization applications. However, a major drawback of particle filters is their computational complexity, which inhibits their use in real-time applications with conventional CPU or DSP based implementation schemes. The re-sampling step in the particle filters creates a computational bottleneck since it is inherently sequential and cannot be parallelized. This paper proposes a modification to the existing particle filter algorithm, which enables parallel re-sampling and reduces the effect of the re-sampling bottleneck. We then present a high-speed and dedicated hardware architecture incorporating pipe-lining and parallelization design strategies to supplement the modified algorithm and lower the execution time considerably. From an application standpoint, we propose a novel source localization model to estimate the position of a source in a noisy environment using the particle filter algorithm implemented on hardware. The design has been prototyped using Artix-7 field-programmable gate array (FPGA), and resource utilization for the proposed system is presented. Further, we show the execution time and estimation accuracy of the high-speed architecture and observe a significant reduction in computational time. Our implementation of particle filters on FPGA is scalable and modular, with a low execution time of about 5.62 µs for processing 1024 particles (compared to 64 ms on Intel Core i7-7700 CPU with eight cores clocking at 3.60 GHz) and can be deployed for real-time applications.

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“…Measurement data association in a cluttered environment is considered to be a high potential and challenging technique in the field of multiple target tracking [ 1 , 2 ]. The main mission of data association is that each measurement obtained by the sensor should be determined whether it belongs to the target when multiple targets are present [ 3 , 4 ]. However, clutters such as false alarms and electronic countermeasures make it very difficult to accomplish the data association mission efficiently.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning-Based Data Association for Multiple Target Tracking in Clutter

Zhang

et al. 2020

Sensors

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Data association is a crucial component of multiple target tracking, in which each measurement obtained by the sensor can be determined whether it belongs to the target. However, many methods reported in the literature may not be able to ensure the accuracy and low computational complexity during the association process, especially in the presence of dense clutters. In this paper, a novel data association method based on reinforcement learning (RL), i.e., the so-called RL-JPDA method, has been proposed for solving the aforementioned problem. In the presented method, the RL is leveraged to acquire available information of measurements. In addition, the motion characteristics of the targets are utilized to ensure the accuracy of the association results. Experiments are performed to compare the proposed method with the global nearest neighbor data association method, the joint probabilistic data association method, the fuzzy optimal membership data association method and the intuitionistic fuzzy joint probabilistic data association method. The results show that the proposed method yields a shorter execution time compared to other methods. Furthermore, it can obtain an effective and feasible estimation in the environment with dense clutters.

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Multi-target tracking method based on improved firefly algorithm optimized particle filter

Cited by 32 publications

References 22 publications

Predicting Green Consumption Behaviors of Students Using Efficient Firefly Grey Wolf-Assisted K-Nearest Neighbor Classifiers

Predicting Green Consumption Behaviors of Students Using Efficient Firefly Grey Wolf-Assisted K-Nearest Neighbor Classifiers

FPGA Implementation of Particle Filters for Robotic Source Localization

Reinforcement Learning-Based Data Association for Multiple Target Tracking in Clutter

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