Group-target Tracking

Geng, Wen-dong; Yuan-qin, Wang; Dong, Zheng-hong

doi:10.1007/978-981-10-1888-6

Cited by 6 publications

(7 citation statements)

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“…The results of centroid tracking are used to assist with the acquisition, synchronization, beamforming, and angle tracking of the receiving antenna in digital signal demodulation. A group target, as defined in [42], comprises multiple targets that meet specific criteria. The characteristics of a group encompass its label, centroid motion state, and overall shape, as outlined in [9].…”

Section: Centroid Tracking In Uav Swarm-based Edge Computingmentioning

confidence: 99%

UAV Swarm Centroid Tracking for Edge Computing Applications Using GRU-Assisted Multi-Model Filtering

Chen,

Liu,

et al. 2024

Electronics

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When an unmanned aerial vehicles (UAV) swarm is used for edge computing, and high-speed data transmission is required, accurate tracking of the UAV swarm’s centroid is of great significance for the acquisition and synchronization of signal demodulation. Accurate centroid tracking can also be applied to accurate communication beamforming and angle tracking, bringing about a reception gain. Group target tracking (GTT) offers a suitable framework for tracking the centroids of UAV swarms. GTT typically involves accurate modeling of target maneuvering behavior and effective state filtering. However, conventional coordinate-uncoupled maneuver models and multi-model filtering methods encounter difficulties in accurately tracking highly maneuverable UAVs. To address this, an innovative approach known as 3DCDM-based GRU-MM is introduced for tracking the maneuvering centroid of a UAV swarm. This method employs a multi-model filtering technique assisted by a gated recurrent unit (GRU) network based on a suitable 3D coordinate-coupled dynamic model. The proposed dynamic model represents the centroid’s tangential load, normal load, and roll angle as random processes, from which a nine-dimensional unscented Kalman filter is derived. A GRU is utilized to update the model weights of the multi-model filtering. Additionally, a smoothing-differencing module is presented to extract the maneuvering features from position observations affected by measurement noise. The resulting GRU-MM method achieved a classification accuracy of 99.73%, surpassing that of the traditional IMM algorithm based on the same model. Furthermore, our proposed 3DCDM-based GRU-MM method outperformed the Singer-KF and 3DCDM-based IMM-EKF in terms of the RMSE for position estimation, which provides a basis for further edge computing.

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Section: Centroid Tracking In Uav Swarm-based Edge Computingmentioning

confidence: 99%

UAV Swarm Centroid Tracking for Edge Computing Applications Using GRU-Assisted Multi-Model Filtering

Chen,

Liu,

et al. 2024

Electronics

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“…The basic theory and framework of multi-target and formation target tracking are mature. Referring to the relevant research results, the general framework of target tracking for groups with dynamic outlines is given in [25]. Its main structure is shown in Figure 1.…”

Section: Tracking Of Low-altitude Small Target Groupsmentioning

confidence: 99%

Robust Tracking for Small Target Groups With Dynamic Outlines

Zhou

Wang

2020

IEEE Access

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Low altitude small target group has the special motion characteristics of orderly as a whole and flexibly as individuals. The performances of traditional target tracking algorithms (such as centroid tracking, geometric centre tracking, etc.) decrease obviously when the group outline changes. Hence in this paper, a robust tracking algorithm for target group with dynamic outline is studied. Aiming at the two key issues of group division and equivalent tracking point estimation, a robust group division algorithm, which can maintain the stability of group division results under any reference point selection, is firstly introduced. Then, on this basis, a new optimal tracking point estimation method is proposed by introducing the group structure into the likelihood function. The simulation results show that this proposed method can effectively improve the robustness of low altitude small target group tracking in the scene of group merging and group splitting. INDEX TERMS Dynamic outlines, equivalent point estimation, group division, group tracking.

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“…We use this motion model to predict the position of the person in the next frame. Then, we use a circular gating function [11] to discard spurious detections and Nearest Neighbor decision using the euclidean distance to perform the final association (Fig. 3).…”

Section: Head Detection and Trackingmentioning

confidence: 99%

Audience measurement using a top-view camera and oriented trajectories

Lopez-Palma

Gago

Corbalan

et al. 2019

IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society

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A crucial aspect for selecting optimal areas for commercial advertising is the probability with which that publicity will be seen. This paper presents a method based on topview camera measurement, where the probability of viewing is estimated according to the trajectories and movements of the head of the passerby individuals in the area of interest. Using a camera with a depth sensor, the head of the people in the range of view can be detected and modelled. That method allows to determine the orientation of the head which is used to estimate the direction of vision. A tracking by detection algorithm is used to compute the trajectory of each user. The attention given at each advertising spot is estimated based on the trajectories and head orientations of the individuals in the area of interest.

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Group-target Tracking

Cited by 6 publications

References 0 publications

UAV Swarm Centroid Tracking for Edge Computing Applications Using GRU-Assisted Multi-Model Filtering

UAV Swarm Centroid Tracking for Edge Computing Applications Using GRU-Assisted Multi-Model Filtering

Robust Tracking for Small Target Groups With Dynamic Outlines

Audience measurement using a top-view camera and oriented trajectories

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