...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Computer vision and image understanding >Efficient multi-target tracking via discovering dense subgraphs
【24h】

Efficient multi-target tracking via discovering dense subgraphs

机译：通过发现密集的子图进行高效的多目标跟踪

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this paper, we cast multi-target tracking as a dense subgraph discovering problem on the undirected relation graph of all given target hypotheses. We aim to extract multiple clusters (dense subgraphs), in which each cluster contains a set of hypotheses of one particular target. In the presence of occlusion or similar moving targets or when there is no reliable evidence for the target's presence, each target trajectory is expected to be fragmented into multiple tracklets. The proposed tracking framework can efficiently link such fragmented target trajectories to build a longer trajectory specifying the true states of the target. In particular, a discriminative scheme is devised via learning the targets' appearance models. Moreover, the smoothness characteristic of the target trajectory is utilised by suggesting a smoothness tracklet affinity model to increase the power of the proposed tracker to produce persistent target trajectories revealing different targets' moving paths. The performance of the proposed approach has been extensively evaluated on challenging public datasets and also in the context of team sports (e.g. soccer, AFL), where team players tend to exhibit quick and unpredictable movements. Systematic experimental results conducted on a large set of sequences show that the proposed approach performs better than the state-of-the-art trackers, in particular, when dealing with occlusion and fragmented target trajectory.
机译：在本文中，我们将多目标跟踪作为所有给定目标假设的无向关系图上的稠密子图发现问题。我们旨在提取多个聚类（密集子图），其中每个聚类包含一个特定目标的一组假设。在存在遮挡物或类似移动目标的情况下，或者在没有可靠证据证明目标存在的情况下，预计每个目标轨迹会被分成多个轨迹。所提出的跟踪框架可以有效地链接这些零碎的目标轨迹，以建立更长的轨迹，从而指定目标的真实状态。特别是，通过学习目标的外观模型来设计区分方案。此外，通过建议平滑轨迹小波亲和力模型来利用目标轨迹的平滑特性，以提高所提出的跟踪器产生持久的目标轨迹的能力，从而揭示不同目标的移动路径。在具有挑战性的公共数据集上以及团队运动（例如足球，AFL）中，团队运动员往往表现出快速且不可预测的动作，因此对该方法的性能进行了广泛的评估。在大量序列上进行的系统实验结果表明，所提出的方法比最新的跟踪器性能更好，特别是在处理遮挡和目标轨迹分散的情况下。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号