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首页> 外文期刊>Pattern Analysis and Machine Intelligence, IEEE Transactions on >Shape-Based Online Multitarget Tracking and Detection for Targets Causing Multiple Measurements: Variational Bayesian Clustering and Lossless Data Association
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Shape-Based Online Multitarget Tracking and Detection for Targets Causing Multiple Measurements: Variational Bayesian Clustering and Lossless Data Association

机译：基于形状的基于目标的在线多目标跟踪和多次测量目标检测：变分贝叶斯聚类和无损数据关联

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摘要

This paper proposes a novel online two-level multitarget tracking and detection (MTTD) algorithm. The algorithm focuses on multitarget detection and tracking for the case of multiple measurements per target and for an unknown and varying number of targets. Information is continuously exchanged in both directions between the two levels. Using the high level target position and shape information, the low level clusters the measurements. Furthermore, the low level features automatic relevance detection (ARD), as it automatically determines the optimal number of clusters from the measurements taking into account the expected target shapes. The high level's data association allows for a varying number of targets. A joint probabilistic data association algorithm looks for associations between clusters of measurements and targets. These associations are used to update the target trackers and the target shapes with the individual measurements. No information is lost in the two-level approach since the measurement information is not summarized into features. The target trackers are based on an underlying motion model, while the high level is supplemented with a filter estimating the number of targets. The algorithm is verified using both simulations and experiments using two sensor modalities, video and laser scanner, for detection and tracking of people and ants.
机译：本文提出了一种新颖的在线两级多目标跟踪与检测（MTTD）算法。该算法专注于多目标检测和跟踪，以针对每个目标进行多次测量以及未知和变化数量的目标。信息在两个级别之间沿两个方向连续交换。使用高级目标位置和形状信息，低级对测量结果进行聚类。此外，低级别功能具有自动相关性检测（ARD）功能，因为它会根据预期目标形状从测量结果中自动确定最佳聚类数。高级别的数据关联允许不同数量的目标。联合概率数据关联算法在测量和目标集群之间寻找关联。这些关联用于通过单独的测量更新目标跟踪器和目标形状。由于没有将测量信息汇总为特征，因此在两级方法中不会丢失任何信息。目标跟踪器基于基础运动模型，而高级跟踪器还补充有一个估计目标数量的过滤器。该算法通过仿真和实验（使用视频和激光扫描仪这两种传感器）对人和蚂蚁的检测和跟踪进行了验证。

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