Detection confidence driven multi-object tracking to recover reliable tracks from unreliable detections
Detection confidence driven multi-object tracking to recover reliable tracks from unreliable detections
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DOI：
10.1016/j.patcog.2022.109107
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Travis Mandel;Mark Jimenez;Emily Risley;Taishi Nammoto;Rebekka Williams;Max Panoff;Meynard Ballesteros;Bobbie Suarez
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文献类型：
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作者：
Travis Mandel;Mark Jimenez;Emily Risley;Taishi Nammoto;Rebekka Williams;Max Panoff;Meynard Ballesteros;Bobbie Suarez
多目标跟踪（MOT）系统通常依赖于精确的目标检测器，然而，精确的检测器并不适用于每个应用领域。我们提出了鲁棒置信度跟踪（RCT），一个离线MOT算法设计的检测质量差的设置。尽管现有方法简单地阈值化并丢弃检测置信度信息，但RCT依赖于精确的检测置信度值来提高贯穿整个跟踪流水线的跟踪质量。这一创新（沿着一些简单且经过充分研究的算法）使RCT能够以最小的身份切换实现稳健的性能，即使在提供完全未过滤的检测时也是如此。为了在不可靠检测的情况下比较跟踪器，我们提出了一个具有挑战性的现实世界的水下鱼类跟踪数据集，FISHTRAC。在对FISHTRAC、UA-DETRAC和MOTChallenge数据的大规模评估中，RCT的表现优于各种跟踪器，包括深度跟踪器和更经典的方法。我们已经在https：//github.com/tmandel/fish-detrac上发布了我们的FISHTRAC代码库和训练数据集，这将有助于在未充分研究的问题上比较跟踪器。
Multi-object tracking (MOT) systems often rely on accurate object detectors; however, accurate detectors are not available in every application domain. We present Robust Confidence Tracking (RCT), an offline MOT algorithm designed for settings where detection quality is poor. Whereas prior methods simply threshold and discard detection confidence information, RCT relies on the exact detection confidence values to increase track quality throughout the entire tracking pipeline. This innovation (along with some simple and well-studied heuristics) allows RCT to achieve robust performance with minimal identity switches, even when provided with completely unfiltered detections. To compare trackers in the presence of unreliable detections, we present a challenging real-world underwater fish tracking dataset, FISHTRAC. In an large-scale evaluation across FISHTRAC, UA-DETRAC, and MOTChallenge data, RCT outperforms a wide variety of trackers, including deep trackers and more classic approaches. We have publically released our FISHTRAC codebase and training dataset at https://github.com/tmandel/fish-detrac, which will facilitate comparing trackers on understudied problems.