多摄像机视觉跟踪的性能评价

L. Marcenaro, Pietro Morerio, C. Regazzoni
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引用次数: 6

摘要

单摄像机多目标视觉跟踪的主要缺点可以通过增加现场收集的信息量来部分消除,即通过增加摄像机。通过采用这种多摄像头方法,多个传感器可以协同工作以实现对整个场景的理解。然而,数据关联和数据融合等新问题也随之出现。这项工作解决了基于Rao-Blackwellized Monte Carlo数据关联(RBMCDA)的多相机跟踪算法在真实数据上的性能评估问题。为此,开发了基于三个性能指标的新度量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance Evaluation of Multi-camera Visual Tracking
Main drawbacks in single-camera multi-target visual tracking can be partially removed by increasing the amount of information gathered on the scene, i.e. by adding cameras. By adopting such a multi-camera approach, multiple sensors cooperate for overall scene understanding. However, new issues arise such as data association and data fusion. This work addresses the issue of evaluating the performance of a multi-camera tracking algorithm based on Rao-Blackwellized Monte Carlo data association (RBMCDA) on real data. For this purpose, a new metric based on three performance indexes is developed.
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