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Tracking Consistency Metric for Video Surveillance Tracking

机译:跟踪视频监控跟踪的一致性度量

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The aim of this paper is to propose an additional performance tracking metric. The performance of a tracking algorithm depends on the parameters that are being tracked ranging from reference points that cover edges and corners, to blob parameters such as size, trajectory and color. Based on different features used in tracking, the tracking metrics range from contingency tables, rates of correct tracks to error differences between tracked features and reference points. Among the tracking metrics that have been developed were tracker detection rate (TDR), object tracking error (OTE) and contingency table. These metrics give a measure of the ability of a tracking algorithm to correctly detect or track a target object. There has been no metric published or utilized to determine the performance of a tracking algorithm in the consistency of maintaining a correct track on the selected target. This paper proposes a method and metric to give a measure of determining the consistency of a tracking. The results of the proposed TC metric have results that range from 0.81 to 0.35 for TDR of 90%, 0.65 to 0.21 for TDR of 80%, 0.5 to 0.16 for TDR of 70%, 0.37 to 0.12 for TDR of 60% and 0.26 to 0.08 for TDR of 50%.
机译:本文的目的是提出额外的性能跟踪度量。跟踪算法的性能取决于从覆盖边缘和角落的参考点进行跟踪的参数,诸如尺寸,轨迹和颜色的BLOB参数。基于跟踪中使用的不同特征,跟踪指标范围从应变表,正确的轨道的速率误差差异与跟踪特征和参考点之间的误差。在开发的跟踪指标中是跟踪器检测率(TDR),对象跟踪错误(OTE)和差管表。这些度量标准给出了跟踪算法正确检测或跟踪目标对象的能力的衡量标准。没有公开或用于确定跟踪算法在所选目标上维护正确轨道的一致性的性能。本文提出了一种方法和指标,以衡量确定跟踪的一致性。所提出的TC度量的结果具有90%,0.65至0.21的0.81至0.35的结果为80%,0.5至0.16的TDR为70%,0.37〜0.12的TDR为60%和0.26 0.08为TDS为50%。

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