论文标题
RLM轨道：在线多培训跟踪，由相对位置映射支持
RLM-Tracking: Online Multi-Pedestrian Tracking Supported by Relative Location Mapping
论文作者
论文摘要
多对象跟踪的问题是一种基本的计算机视觉研究重点，广泛用于公共安全，运输，自动驾驶汽车，机器人技术以及涉及人工智能的其他地区。由于自然场景的复杂性，对象阻塞和半封闭通常会发生在基本跟踪任务中。这些很容易导致ID切换，对象丢失，检测错误和未对准的限制框。这些条件对多对象跟踪的精度有重大影响。在本文中，我们为上述问题设计了一个新的多对象跟踪器，其中包含一个对象\ textbf {相对位置映射}（rlm）模型和\ textbf {target区域密度}（trd）模型。新跟踪器对对象之间的位置关系差异更为敏感。根据视频中对象区域的密度，它可以实时将低分检测框引入不同区域。这提高了对象跟踪的准确性，而无需消耗大量的算术资源。我们的研究表明，当应用于高级MOT方法时，提出的模型已大大提高了MOT17和MOT20数据集的HOTA和DF1测量值。
The problem of multi-object tracking is a fundamental computer vision research focus, widely used in public safety, transport, autonomous vehicles, robotics, and other regions involving artificial intelligence. Because of the complexity of natural scenes, object occlusion and semi-occlusion usually occur in fundamental tracking tasks. These can easily lead to ID switching, object loss, detect errors, and misaligned limitation boxes. These conditions have a significant impact on the precision of multi-object tracking. In this paper, we design a new multi-object tracker for the above issues that contains an object \textbf{Relative Location Mapping} (RLM) model and \textbf{Target Region Density} (TRD) model. The new tracker is more sensitive to the differences in position relationships between objects. It can introduce low-score detection frames into different regions in real-time according to the density of object regions in the video. This improves the accuracy of object tracking without consuming extensive arithmetic resources. Our study shows that the proposed model has considerably enhanced the HOTA and DF1 measurements on the MOT17 and MOT20 data sets when applied to the advanced MOT method.
