基于密度图多目标追踪的时空数据可视化  

Spatiotemporal data visualization based on density map multi-target tracking

作　　者：宋思程 陈辰 李晨辉[1] 王长波[1] SONG Sicheng;CHEN Chen;LI Chenhui;WANG Changbo(School of Computer Science and Technology,East China Normal University,Shanghai 200062,China;Shanghai Institute of Artificial Intelligence for Education,East China Normal University,Shanghai 200062,China;Software Engineering Institute,East China Normal University,Shanghai 200062,China)

机构地区：[1]华东师范大学计算机科学与技术学院,上海200062 [2]华东师范大学上海智能教育研究院,上海200062 [3]华东师范大学软件工程学院,上海200062

出　　处：《图学学报》2024年第6期1289-1300,共12页Journal of Graphics

基　　金：国家自然科学基金(62072183);上海自然科学基金面上项目(24ZR1418300);上海市长三角科技创新共同体领域项目(23002400400)。

摘　　要：时空数据追踪的可视化问题已经受到了广泛的关注,其研究重点在于展示数据的动态细节,并确保轨迹与观测结果的一致性。为此,提出了一种融合深度学习与传统追踪技术的模型,用于执行追踪任务,从而提高可视化的速度和准确度。首先,生成一个高质量的柏林噪声数据集并在该数据集上训练了一个多目标追踪模型。其次,提出了双阶段、多模型的深度学习框架来增强对动态场景的分析深度。最后,为了能够连续地展现详尽的追踪信息,提出了一种可以增强追踪信息的视觉效果结合轨迹和矢量场的可视化解决方案。在不同的案例中展示了该方法的有用性和鲁棒性,并从多个方面进行了量化评估和比较。结果表明该方法可以帮助用户在不同场景中理解多目标追踪信息。The spatiotemporal data tracking visualization has received widespread attention.The focus of this research is on depicting the dynamic details of the data and ensuring trajectory consistency with the observation results.In this paper,a model that combined deep learning with traditional tracking techniques was proposed to perform tracking tasks,thereby improving the speed and accuracy of visualization.First,a high-quality Perlin noise dataset was generated,on which a multi-target tracking model was trained.Second,a two-stage,multi-model deep learning framework was proposed to enhance the analysis depth of dynamic scenes.Finally,in order to continuously display detailed tracking information,a visualization solution that combined trajectories and vector fields was introduced to enhance the visual effect of tracking information.Different cases in this study demonstrated the usefulness and robustness of the proposed method,quantitatively evaluating and omparing the method from multiple aspects.The results showed that the method proposed in this study can help users in understanding multi-target tracking information in different scenarios.

关 键 词：照数据可视化 深度学习 时空数据 多目标跟踪 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]