Computer Model for Evaluating Multi-Target Tracking Algorithms

Garret Vo, Chiwoo Park
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Abstract

Public benchmark datasets have been widely used to evaluate multi-target tracking algorithms. Ideally, the benchmark datasets should include the video scenes of all scenarios that need to be tested. However, a limited amount of the currently available benchmark datasets does not comprehensively cover all necessary test scenarios. This limits the evaluation of multitarget tracking algorithms with various test scenarios. This paper introduced a computer simulation model that generates benchmark datasets for evaluating multi-target tracking algorithms with the complexity of multitarget tracking scenarios directly controlled by simulation inputs such as target birth and death rates, target movement, the rates of target merges and splits, target appearances, and image noise types and levels. The simulation model generated a simulated video and also provides the ground-truth target tracking for the simulated video, so the evaluation of multitarget tracking algorithms can be easily performed without any manual video annotation process. We demonstrated the use of the proposed simulation model for evaluating tracking-by-detection algorithms and filtering-based tracking algorithms.
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评价多目标跟踪算法的计算机模型
公共基准数据集已被广泛用于评估多目标跟踪算法。理想情况下,基准数据集应该包括需要测试的所有场景的视频场景。然而,目前可用的基准数据集数量有限,不能全面覆盖所有必要的测试场景。这限制了多目标跟踪算法在各种测试场景下的评估。本文介绍了一种计算机仿真模型,该模型生成基准数据集,用于评估多目标跟踪算法,多目标跟踪场景的复杂性由仿真输入直接控制,如目标的出生率和死亡率、目标的运动、目标的合并和分裂率、目标的外观以及图像噪声的类型和水平。该仿真模型生成了仿真视频,并为仿真视频提供了真实目标跟踪,因此无需手动视频注释过程即可方便地对多目标跟踪算法进行评估。我们演示了使用所提出的仿真模型来评估检测跟踪算法和基于滤波的跟踪算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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