Occlusion-Aware Correlation Particle Filter Target Tracking Based on RGBD Data

Zhai, Yayu; Song, Ping; Mou, Zonglei; Chen, Xiaoxiao; Liu, Xiongjun

doi:10.1109/access.2018.2869766

Cited by 13 publications

(4 citation statements)

References 24 publications

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“…Generally, the position and velocity of the target are taken as the state, i.e. x(t) col{x p (t), ẋp (t), y p (t), ẏp (t)}, and the motion process of the target can be modeled by (13), where the specific parameters are given as follows [38]…”

Section: A Linear Target Motion Systemmentioning

confidence: 99%

“…Since the target tracking problem is essentially a nonlinear state estimation problem, a variety of nonlinear filters have been developed in [9]- [13] to track the mobile target. Specifically, the extended Kalman filter (EKF) [14] usually linearized the nonlinear process and measurement model by Taylor series expansion, then the classical Kalman filter (KF) can be used to estimate the target's position.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Fusion Tracking Estimation Under Range-Only Measurement

2022

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This paper is concerned with the distributed fusion estimation problem of range-only target tracking system with unknown but bounded noises, where the linear and nonlinear motion models are both considered. Particularly, a kind of nonlinear transformation is used to convert the nonlinear distance measurement model into a linear one, which eliminates the corresponding linearization errors in the design of estimation error system. In spite of the transformed measurement noise becomes more complicated, while it is still bounded. Moreover, for the nonlinear target motion model, the state linearization error caused by the Taylor expansion is modeled by the state dependent matrix with uncertainty bounded matrix. In this case, based on the bounded recursive optimization algorithm, two kinds of convex optimization problems are established to determine the gains of the local/fusion estimators, and the stability of the designed estimators also can be guaranteed. Finally, two different range-only target tracking systems are presented to show the effectiveness and advantages of the proposed methods.

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Section: A Linear Target Motion Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed Fusion Tracking Estimation Under Range-Only Measurement

2022

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“…Nevertheless, its computational speed and accuracy could not be optimal in case of the elaborate environments. In [23]- [25], Particle filter-based vehicle tracking via HOG features is very robust when fast-moving human target but this algorithm is hard to integrate with a mobile system. In [26] and [27], the Camshift algorithm is well-known as popular tracking human method.…”

Section: Introductionmentioning

confidence: 99%

Real-time Target Human Tracking using Camshift and LucasKanade Optical Flow Algorithm

Nguyen¹,

Nguyen²,

Bui³

et al. 2021

Adv. sci. technol. eng. syst. j.

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In this paper, a novel is proposed for real-time tracking human targets in cases of high influence from complexity environment with a normal camera. Firstly, based on Oriented FAST and Rotated BRIEF features, the Lucas-Kanade Optical Flow algorithm is used to track reliable keypoints. This method represents a valuable performance to decline the effect of the illumination or displacement of human targets. Secondly, the area of the human target in the frame is determined more precise by using the Camshift algorithm. Compared to the existing approaches, the proposed method has some merits to some extents including rapid calculations in implementation, high accuracy in case of similar objects detection, the ability to deploy easily on mobile devices. Finally, the effectiveness of the proposed tracking algorithm is demonstrated via experimental results.

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“…In addition, there are some methods to restore the occluded part through binocular information (Camuñas-Mesa et al, 2017), but this kind of method generally can only restore the occluded edge part. Because the occlusion problem is complex, some researchers combine different methods to design a framework to restore the occlusion state or identify the degree of occlusion (Liu et al, 2020;Zhai et al, 2018;Wang et al, 2020;Zhan et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Predictive visual control network for occlusion solution in human-following robot

Zou

2021

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Purpose The purpose of this paper is to propose a new video prediction-based methodology to solve the manufactural occlusion problem, which causes the loss of input images and uncertain controller parameters for the robot visual servo control. Design/methodology/approach This paper has put forward a method that can simultaneously generate images and controller parameter increments. Then, this paper also introduced target segmentation and designed a new comprehensive loss. Finally, this paper combines offline training to generate images and online training to generate controller parameter increments. Findings The data set experiments to prove that this method is better than the other four methods, and it can better restore the occluded situation of the human body in six manufactural scenarios. The simulation experiment proves that it can simultaneously generate image and controller parameter variations to improve the position accuracy of tracking under occlusions in manufacture. Originality/value The proposed method can effectively solve the occlusion problem in visual servo control.

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Occlusion-Aware Correlation Particle Filter Target Tracking Based on RGBD Data

Cited by 13 publications

References 24 publications

Distributed Fusion Tracking Estimation Under Range-Only Measurement

Distributed Fusion Tracking Estimation Under Range-Only Measurement

Real-time Target Human Tracking using Camshift and LucasKanade Optical Flow Algorithm

Predictive visual control network for occlusion solution in human-following robot

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