Target tracking based on the cognitive associative network

Xiu, Chunbo; Chai, Zuohong

doi:10.1049/iet-ipr.2018.5461

Cited by 6 publications

(4 citation statements)

References 21 publications

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“…We give the iteration to obtain optimal policy and the deterministic policy is given by (31) where Q( * ) denotes the value function, β is the behavior policy.…”

Section: Ddpg-based Scheduling Strategymentioning

confidence: 99%

Intelligent Resource Collaboration in Mobile Target Tracking Oriented Mission-Critical Sensor Networks

et al. 2020

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Mobile target tracking-oriented sensor networks are a special kind of Mission-critical Sensor Networks (MCSN), in which the various missions with the diverse priorities exist. However, it is challenging to achieve real time tracking while keeping the MCSN a long life time with limited energy provision in a complicated environment. In this paper, we develop a collaborative perception and intelligent scheduling scheme, which jointly optimizes the system responding latency and tracking accuracy with the constraint of the available energy. A new hierarchical architecture is proposed to realize the coupled function of perception and computation. In particular, the multi-node collaborative perception scheme is applied to obtain the excellent sensing capacity, and the Unmanned Aerial Vehicles (UAVs) play as the edge nodes to provide the computing service for those resource-constrained sensor nodes. To reach the sustained target tracking, we propose an intelligent tracking policy by exploiting the deep deterministic policy gradient (DDPG) method. Simulation results demonstrate that the proposed intelligent collaboration scheme can improve the tracking accuracy by 45.5% compared with the random selection scheme. The system cost is also reduced approximately by 17.3% while guaranteeing the tracking accuracy. INDEX TERMS Multi-target tracking, energy consumption, collaborative perception, deep reinforcement learning.

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“…We give the iteration to obtain optimal policy and the deterministic policy is given by (31) where Q( * ) denotes the value function, β is the behavior policy.…”

Section: Ddpg-based Scheduling Strategymentioning

confidence: 99%

Intelligent Resource Collaboration in Mobile Target Tracking Oriented Mission-Critical Sensor Networks

et al. 2020

View full text Add to dashboard Cite

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“…Currently, there are two main tracking methods, including methods based on Deep Learning (DL) [5][6][7][8][9][10][11][12][13] and methods based on Discriminative Correlation Filtering (DCF) [14 -21]. The former leverages neural networks to obtain more sophisticated depth features, which have proven to be effective for subsequent tracking.…”

Section: Introductionmentioning

confidence: 99%

AD-SiamRPN: Anti-Deformation Object Tracking via an Improved Siamese Region Proposal Network on Hyperspectral Videos

et al. 2023

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Object tracking using Hyperspectral Images (HSIs) obtains satisfactory result in distinguishing objects with similar colors. Yet, the tracking algorithm tends to fail when the target undergoes deformation. In this paper, a SiamRPN based hyperspectral tracker is proposed to deal with this problem. Firstly, a band selection method based on a genetic optimization method is designed for rapidly reducing the redundancy of information in HSIs. Specifically, three bands with highest joint entropy are selected. To solve the problem that the information of the template in the SiamRPN model decays over time, an update network is trained on the dataset from general objective tracking benchmark, which can obtain effective cumulative templates. The use of cumulative templates with spectral information makes it easier to track the deformed target. In addition, transfer learning of the pre-trained SiamRPN is designed to obtain a better model for HSIs. The experimental results show that the proposed tracker can obtain good tracking results over the entire public dataset, and that it is better than the other popular trackers when the target’s deformation is qualitatively and quantitatively compared, achieving an overall success rate of 57.5% and a deformation challenge success rate of 70.8%.

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“…Generally, there are two types of visual tracking methods, involving discriminative correlation filter (DCF) based methods [3, 9–12, 14, 39, 43] and generative deep learning (DL) based methods [2, 15, 17, 19, 25, 29, 30, 45]. The DCF trains filters [20, 23] efficiently in the frequency domain via fast Fourier transform (FFT).…”

Section: Introductionmentioning

confidence: 99%

“…Deep features automatically capture the intrinsic properties of objects in different scenes. These improved DCF trackers attempt to solve the occlusion problem [45] of the original MOSSE method. Nevertheless, these discriminative methods are also sensitive to the false target with colour similarity.…”

Section: Introductionmentioning

confidence: 99%

GOMT: Multispectral video tracking based on genetic optimization and multi‐features integration

Qian

Chen

Dong³

2023

IET Image Processing

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Traditional visual tracking algorithms on RGB cannot effectively distinguish an object from background with similar colour feature, resulting in the migration problem in the tracking process. In order to solve this problem, a tracking algorithm with spectral information is proposed by using band selection, information fusion, and deep features. Firstly, the correlation analysis of 16 spectral band information is carried out, and genetic optimization method is introduced to obtain two bands with low correlation coefficient and abundant information. Moreover, under the framework of kernel correlation filtering tracking, guided filtering is adopted to fuse the information referring to the target region of the two bands. Besides, the feature maps are generated by histogram of gradient and pretrained visual geometry group network. Finally, target is detected via finding the maximum value of a strong response. The proposed genetic optimization based multifeature tracker is compared with multiple tracking methods including correlation filtering and deep learning. Experimental results with multiple groups of spectral videos and corresponding RGB videos demonstrate that the genetic optimization based multifeature tracker method achieves good results in subjective vision and objective evaluation.

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Target tracking based on the cognitive associative network

Cited by 6 publications

References 21 publications

Intelligent Resource Collaboration in Mobile Target Tracking Oriented Mission-Critical Sensor Networks

Intelligent Resource Collaboration in Mobile Target Tracking Oriented Mission-Critical Sensor Networks

AD-SiamRPN: Anti-Deformation Object Tracking via an Improved Siamese Region Proposal Network on Hyperspectral Videos

GOMT: Multispectral video tracking based on genetic optimization and multi‐features integration

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