论文标题
samot：切换器感知的多对象跟踪，还有另一种MOT措施
SAMOT: Switcher-Aware Multi-Object Tracking and Still Another MOT Measure
论文作者
论文摘要
多对象跟踪（MOT）是计算机视觉中的流行主题。但是，身份问题，即，一个对象与另一个身份的另一个对象错误关联，仍然是一个具有挑战性的问题。为了解决这个问题，切换器，即混淆了可能引起身份问题的目标。基于这种动机，本文提出了一个新颖的开关感知器框架，用于多对象跟踪，该框架由空间冲突图模型（SCG）和切换器感知关联（SAA）组成。 SCG通过构建冲突图并确定最佳子图来消除一个框架内的空间切换器。 SAA利用跨帧的潜在时间切换器中的其他信息，从而实现了更准确的数据关联。此外，我们提出了一种新的MOT评估措施，还有另一种IDF分数（SAIDF），旨在更多地关注身份问题。此新措施可能会克服以前措施的某些问题，并为MOT中的身份问题提供更好的见解。最后，根据传统措施和我们提出的新措施对提出的框架进行了测试。广泛的实验表明，OurMethod在所有度量上都取得了竞争成果。
Multi-Object Tracking (MOT) is a popular topic in computer vision. However, identity issue, i.e., an object is wrongly associated with another object of a different identity, still remains to be a challenging problem. To address it, switchers, i.e., confusing targets thatmay cause identity issues, should be focused. Based on this motivation,this paper proposes a novel switcher-aware framework for multi-object tracking, which consists of Spatial Conflict Graph model (SCG) and Switcher-Aware Association (SAA). The SCG eliminates spatial switch-ers within one frame by building a conflict graph and working out the optimal subgraph. The SAA utilizes additional information from potential temporal switcher across frames, enabling more accurate data association. Besides, we propose a new MOT evaluation measure, Still Another IDF score (SAIDF), aiming to focus more on identity issues.This new measure may overcome some problems of the previous measures and provide a better insight for identity issues in MOT. Finally,the proposed framework is tested under both the traditional measures and the new measure we proposed. Extensive experiments show that ourmethod achieves competitive results on all measure.
