Quantized genetic resampling particle filtering for vision-based ground moving target tracking

Wang, Yu; Wang, Xiaogang; Shan, Yongzhi; Cui, Naigang

doi:10.1016/j.ast.2020.105925

Cited by 17 publications

(6 citation statements)

References 38 publications

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“…In the above literature, most of the research articles [6], [9]- [13], [37] concentrate on measurement quantization and addressed the tracking/fusion problem under low bandwidth scenarios. Further, most of the contributions considers range information, which is easy to quantize.…”

Section: ) Sensor Fusionmentioning

confidence: 99%

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Distributed Fusion of Optimally Quantized Local Tracker Estimates for Underwater Wireless Sensor Networks

et al. 2022

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Multi-sensor underwater surveillance has been a significant research problem for civilian and naval applications. Due to limited bandwidth considerations, the underwater wireless sensor networks (UWSNs) use measurement quantization to transmit information from individual sensors to the fusion center to perform centralized tracking/fusion. However, at the measurement level, quantization of azimuth information is complex due to its non-linear behavior. To address this problem, this paper proposes to perform the distributed tracking and quantizing the local estimates (state and covariance) to provide improved bandwidth and reduce computational load. The local tracker estimates the updated state and covariance of a target's time-varying dynamics in the given surveillance from the obtained measurements using extended Kalman filter (EKF) and global nearest neighbor (GNN) data association. The measurement model contains both detections of target and false alarms. This paper uses optimal quantization rather than linear quantization owing to its minimal bandwidth requirement. Once the quantized local tracks are obtained at the fusion center, these tracks are quantified using track-to-track association (T2TA) in the S-D assignment framework. The associated tracks are fused using correlation-free fusion algorithms like covariance intersect (CI), sampling covariance intersects (SCI), ellipsoidal intersect (EI), and arithmetic average (AA) algorithms to achieve the global track. The position root mean square error (PRMSE), bandwidth, and error ellipses are used to quantify the performance of the proposed framework. The simulation results show that the PRMSE of the optimally quantized fusion estimates yields good agreement with the unquantized method. Simulation results further reveals that, optimal quantization utilizes lower bandwidth compared to linear quantization. In addition, optimally quantized local estimates accomplishes promising covariance regions at the fusion center.INDEX TERMS Correlation-free fusion, extended Kalman filter, state quantization, target tracking, trackto-track association.

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Section: ) Sensor Fusionmentioning

confidence: 99%

“…Based on the collected measurements, the local tracker estimates the state and covariance of targets. Traditionally, the measurements are quantized and sent to the fusion node [6], [9]- [13], [37], [38]. The Local estimates are quantized and then sent to the fusion node to obtain fused/global estimates.…”

Section: Problem Formulationmentioning

confidence: 99%

Distributed Fusion of Optimally Quantized Local Tracker Estimates for Underwater Wireless Sensor Networks

et al. 2022

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“…Camera-based visual tracking is used in multiple applications ranging from automated surveillance to object tracking and medical care. Since the surrounding environment continuously changes when an object moves, studies are being conducted on object tracking that is robust despite changes in lighting, background confusion, and scale [15][16][17][18]. In the context of autonomous driving and vehicle navigation, researchers are actively engaged in refining location estimation through particle filter-based approaches for autonomous vehicles and robots.…”

Section: Introductionmentioning

confidence: 99%

A Sliding Mode Approach-Based Adaptive Steering Control Algorithm for Path Tracking of Autonomous Mobility with Weighted Injection

Kim,

2023

Machines

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The increasing complexity of mathematical models developed as part of the recent advancements in autonomous mobility platforms has led to an escalation in uncertainty. Despite the intricate nature of such models, the detection, decision, and control methods for autonomous mobility path tracking remain critical. This study aims to achieve path tracking based on pixel-based control errors without parameters in the mathematical model. The proposed approach entails deriving control errors from a multi-particle filter based on a camera, estimating the error dynamics coefficients through a recursive least squares (RLS) approach, and using the sliding mode approach and weighted injection to formulate a cost function that leverages the estimated coefficients and control errors. The resultant adaptive steering control expedites the convergence of control errors towards zero by determining the magnitude of the injection variable based on the control errors and the finite-time convergence condition. The efficacy of the proposed approach is evaluated through an S-curved and elliptical path using autonomous mobility equipped with a single steering and driving module. The results demonstrate the capability of the approach to reasonably track target paths through driving and steering control facilitated by a multi-particle filter and a lidar-based obstacle detection system.

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“…Recently, scholars have proposed many tracking algorithms to meet the requirements of the dim and small target tracking, which can be divided into two main categories including correlation filters-based methods [12][13][14] and PF-based methods [15][16][17][18][19]. KCF has received the most attention in the methods based on correlation filters for dim and small target tracking, which is achieved by establishing a discriminator based on the correlation operator with a kernel function.…”

Section: Introductionmentioning

confidence: 99%

Dim and Small Target Tracking Using an Improved Particle Filter Based on Adaptive Feature Fusion

et al. 2022

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Particle filters have been widely used in dim and small target tracking, which plays a significant role in navigation applications. However, their characteristics, such as difficulty of expressing features for dim and small targets and lack of particle diversity caused by resampling, lead to a considerable negative impact on tracking performance. In the present paper, we propose an improved resampling particle filter algorithm based on adaptive multi-feature fusion to address the drawbacks of particle filters for dim and small target tracking and improve the tracking performance. We first establish an observation model based on the adaptive fusion of the features of the weighted grayscale intensity, edge information, and wavelet transform. We then generate new particles based on residual resampling by combining the target position in the previous frame and the particles in the current frame with higher weights, with the tracking accuracy and particle diversity improving simultaneously. The experimental results demonstrate that our proposed method achieves a high tracking performance with a distance accuracy of 77.2% and a running speed of 106 fps, respectively, meaning that it will have a promising prospect in dim and small target tracking applications.

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Quantized genetic resampling particle filtering for vision-based ground moving target tracking

Cited by 17 publications

References 38 publications

Distributed Fusion of Optimally Quantized Local Tracker Estimates for Underwater Wireless Sensor Networks

Distributed Fusion of Optimally Quantized Local Tracker Estimates for Underwater Wireless Sensor Networks

A Sliding Mode Approach-Based Adaptive Steering Control Algorithm for Path Tracking of Autonomous Mobility with Weighted Injection

Dim and Small Target Tracking Using an Improved Particle Filter Based on Adaptive Feature Fusion

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