School of Engineering, RMIT University, Melbourne, VIC 3000, Australia.
School of Science, RMIT University, Melbourne, VIC 3000, Australia.
Sensors (Basel). 2020 Feb 10;20(3):929. doi: 10.3390/s20030929.
One of the core challenges in visual multi-target tracking is occlusion. This is especially important in applications such as video surveillance and sports analytics. While offline batch processing algorithms can utilise future measurements to handle occlusion effectively, online algorithms have to rely on current and past measurements only. As such, it is markedly more challenging to handle occlusion in online applications. To address this problem, we propagate information over time in a way that it generates a sense of déjà vu when similar visual and motion features are observed. To achieve this, we extend the Generalized Labeled Multi-Bernoulli (GLMB) filter, originally designed for tracking point-sized targets, to be used in visual multi-target tracking. The proposed algorithm includes a novel false alarm detection/removal and label recovery methods capable of reliably recovering tracks that are even lost for a substantial period of time. We compare the performance of the proposed method with the state-of-the-art methods in challenging datasets using standard visual tracking metrics. Our comparisons show that the proposed method performs favourably compared to the state-of-the-art methods, particularly in terms of ID switches and fragmentation metrics which signifies occlusion.
视觉多目标跟踪的核心挑战之一是遮挡。在视频监控和运动分析等应用中，这一点尤为重要。虽然离线批量处理算法可以利用未来的测量结果来有效地处理遮挡，但在线算法只能依靠当前和过去的测量结果。因此，在在线应用中处理遮挡更加具有挑战性。为了解决这个问题，我们在时间上传播信息，以便在观察到相似的视觉和运动特征时产生似曾相识的感觉。为此，我们扩展了最初为跟踪点状目标而设计的广义标记多伯努利（GLMB）滤波器，以便在视觉多目标跟踪中使用。所提出的算法包括一种新颖的虚假警报检测/去除和标签恢复方法，能够可靠地恢复甚至丢失了相当长一段时间的跟踪。我们使用标准的视觉跟踪指标在具有挑战性的数据集上比较了所提出的方法与最先进方法的性能。我们的比较表明，与最先进的方法相比，所提出的方法表现良好，尤其是在 ID 切换和表示遮挡的碎片化指标方面。
