Localization and tracking of closely-spaced human targets based on infrared sensors

Yang, Bo‐Suk; Zhang, Yukexin

doi:10.1016/j.infrared.2022.104176

Cited by 3 publications

(4 citation statements)

References 15 publications

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“… Calculate the association probability with NJPDA method Calculate

Using both equations [ 8 , 17 ]; Calculate

and

using equation [ 12 ] and equation [ 13 ] respectively. Put the results calculated respectively from steps 1) and 2) into equation [ 11 ] for iteration, and use the results from each iteration to evaluate the network energy with equation [ 5 ]. If

, where

represents iteration number and

is a fixed threshold value, or the iteration reaches a predefined number of iterations, then end the iteration and output the optimal association probability,

, i.e.…”

Section: The Direction Information Fused Njpda Methodsmentioning

confidence: 99%

“…Using this technology, the moving targets in land, sea, air and space can be warned or tracked in time, and their moving states can be estimated and analyzed, so as to provide basic information for fire control, threat estimation, situation assessment and decision-making of command and control systems at all levels. In recent years, people have done a lot of researches on multi-target tracking and put forward many meaningful algorithms [ [6] , [7] , [8] , [9] , [10] , [11] , [12] ]. The core problem of multi-target tracking is how to carry out the effective data association [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

“…There are many commonly used methods for processing data association, such as nearest neighbor, joint probability data association (JPDA) [ 15 ], multiple hypotheses, multi-dimensional allocation, fuzzy and neural networks, among which JPDA is a special algorithm for multi-target tracking. Because of its excellent performance in the multi-target tracking under dense clutter, it has attracted great attention once proposed, and is considered to be one of the most effective and reliable means to track multiple targets [ 11 , 16 ]. However, with the increase of the number of targets and clutters, the number of joint association assumptions increases sharply, and the amount of calculation appears the phenomenon of combined explosion, which limits its wide practical application [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Research on multi-target data association of the infrared fish-eye warning system

Zhou,

Fang,

et al. 2023

Heliyon

View full text Add to dashboard Cite

“… Calculate the association probability with NJPDA method Calculate

Using both equations [ 8 , 17 ]; Calculate

and

, where

represents iteration number and

is a fixed threshold value, or the iteration reaches a predefined number of iterations, then end the iteration and output the optimal association probability,

, i.e.…”

Section: The Direction Information Fused Njpda Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Research on multi-target data association of the infrared fish-eye warning system

Zhou,

Fang,

et al. 2023

Heliyon

View full text Add to dashboard Cite

“…Yet, at the center of every navigation module lies a localization framework, the purpose of which is to determine the platforms' position relative to their environment. In the vast variety of related approaches, visual localization [1] holds a vital role and can offer an edge over the alternatives [2][3][4], depending on the nature of the underlying problem.…”

Section: Introductionmentioning

confidence: 99%

Visual Place Recognition in Changing Environments with Sequence Representations on the Distance-Space Domain

et al. 2023

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Navigating in a perpetually changing world can provide the basis for numerous challenging autonomous robotic applications. With a view to long-term autonomy, visual place recognition (vPR) systems should be able to robustly operate under extreme appearance changes in their environment. Typically, the utilized data representations are heavily influenced by those changes, negatively affecting the vPR performance. In this article, we propose a sequence-based technique that decouples such changes from the similarity estimation procedure. This is achieved by remapping the sequential representation data into the distance-space domain, i.e., a domain in which we solely consider the distances between image instances, and subsequently normalize them. In such a way, perturbations related to different environmental conditions and embedded into the original representation vectors are avoided, therefore the scene recognition efficacy is enhanced. We evaluate our framework under multiple different instances, with results indicating a significant performance improvement over other approaches.

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Railway foreign object tracking and detection with spatial positioning and feature generalization enhancement

Yong,

Zhen,

Fangchun

2024

AAI

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The existing deep learning foreign object tracking and detection algorithm is easily affected by complex environments and target occlusion, resulting in problems such as missed detection and low detection accuracy. A railway foreign object tracking and detection algorithm with spatial positioning and feature generalization enhancement is proposed. First, a multi-scale cascade improved GhostNet feature network is proposed to improve the feature extraction capability of infrared targets. Secondly, the spatial positioning and feature generalization enhancement module is designed by using the spatial position positioning and generalized morphological information of foreign objects to enhance the detection accuracy of targets with position movement and tracking trajectory changesin complex scenes. Then, a pyramid prediction network is constructed to obtain the detection anchor frame, type and confidence information of infrared railway foreign objects. Finally, by improving the DeepSORT tracking algorithm with improved category and confidence display, combined with Kalman filtering and Hungarian algorithm, railway foreign object tracking and detection in infrared weak light environment is realized. Experimental results show that the proposed method has a tracking and detection accuracy of railway foreign objects 83.3%, with an average detection rate of 11.3 frames per second. Compared with the comparison method, the proposed method has higher detection accuracy and has better performance in railway foreign object tracking and detection in infrared weak light scenes.

show abstract

Localization and tracking of closely-spaced human targets based on infrared sensors

Cited by 3 publications

References 15 publications

Research on multi-target data association of the infrared fish-eye warning system

Research on multi-target data association of the infrared fish-eye warning system

Visual Place Recognition in Changing Environments with Sequence Representations on the Distance-Space Domain

Railway foreign object tracking and detection with spatial positioning and feature generalization enhancement

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