分布式足球训练智能传感器多目标定位与跟踪

J. Sensors Pub Date : 2022-08-05 DOI:10.1155/2022/4772636
Jian Jiang, Zhiqun Qiu
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引用次数: 1

摘要

本文采用分布式智能传感器方法对足球训练中多目标的定位和跟踪进行了深入的研究和分析。采用事件触发机制驱动分布式声阵列传感器网络中的声阵列传感器,解决了麦克风频繁通信带来的通信负荷增加的问题,有效降低了麦克风之间的通信负荷,降低了声阵列传感器网络的能耗。通过设计合适的声源目标状态估计方程,充分利用其节点的测量和状态估计信息以及相邻节点的状态估计信息,可以准确预测声源目标的下一时刻状态。提出了一种基于多尺度空间共定位的相关滤波跟踪算法。在该算法中,跟踪器总共包含多个采样范围不同的子滤波器。然后,本文还提出了一种协同判别方法,通过判断各滤波器的目标图像样本的空间响应,共同在线定位目标。在此基础上,本文进一步探讨了相关滤波跟踪算法在复杂环境中的潜力,提出了一种融合多尺度空间视图的鲁棒相关滤波跟踪算法。提出了基于多帧姿态信息的横视几何相似度度量,匹配效果优于单帧横视几何相似度度量;为了解决球员外观相似度干扰问题,进一步提出了一种基于图模型的跨视图外观相似度度量学习方法,以每个视图中的球员为节点,球员外观深度特征为节点属性,以跨视图球员之间的连接为边,构建跨视图球员图。图卷积神经网络训练得到的相似度优于基于简单余弦距离计算的外观相似度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Distributed Soccer Training Smart Sensors for Multitarget Localization and Tracking
This paper presents an in-depth study and analysis of the localization and tracking of multiple targets in soccer training using a distributed intelligent sensor approach. An event-triggered mechanism is used to drive the acoustic array sensors in the distributed acoustic array sensor network, which solves the problem of increased communication load caused by frequent communication of microphones and effectively reduces the communication load between microphones as well as the energy consumption of the acoustic array sensor network. By designing a suitable state estimation equation for the acoustic source target and fully utilizing the measurement and state estimation information of its nodes as well as the state estimation information of neighboring nodes, the next moment state of the acoustic source target can be accurately predicted. A correlation filtering tracking algorithm based on multiscale spatial co-localization is proposed. In the proposed algorithm, the tracker contains a total of several subfilters with different sampling ranges. Then, this paper also proposes a collaborative discrimination method to judge the spatial response of the target image samples of each filter and jointly localize the target online. Based on this, this paper further explores the potential of correlation filter tracking algorithms in complex environments and proposes a robust correlation filter tracking algorithm that fuses multiscale spatial views. The cross-view geometric similarity measure based on multiframe pose information is proposed, and the matching effect is better than that based on single-frame cross-view geometric similarity; to solve the problem of player appearance similarity interference, a graph model-based cross-view appearance similarity measure learning method is further proposed, with players in each view as nodes, player appearance depth features as node attributes, and connections between cross-view players as edges to construct a cross-view player graph. The similarity obtained by the graph convolutional neural network training is better than the appearance similarity calculated based on simple cosine distance.
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