论文标题
SOMPT22：以监视为导向的多人跟踪数据集
SOMPT22: A Surveillance Oriented Multi-Pedestrian Tracking Dataset
论文作者
论文摘要
由于卷积神经网络（CNN）在过去十年中检测到成功，多对象跟踪（MOT）通过检测方法的使用来控制。随着数据集和板凳标记网站的发布，研究方向已转向在跟踪时在包括重新识别对象的通用场景（包括重新识别（REID））上的最佳准确性。在这项研究中，我们通过提供专用的行人数据集，并专注于对表现良好的多对象跟踪器的深入分析，以观察到现实世界应用的最弱和强的技术。为此，我们介绍了SOMPT22数据集；一套新的套装，用于多人跟踪，带有带注释的简短视频，该视频从位于杆子上的静态摄像头捕获，高度为6-8米，用于城市监视。与公共MOT数据集相比，这提供了室外监视MOT的更为集中和具体的基准。我们分析了该新数据集中检测和REID网络的使用方式分类为单发和两阶段的MOT跟踪器。我们新数据集的实验结果表明，SOTA远非高效率，而单一跟踪器是统一快速执行和具有竞争性能的准确性的良好候选者。该数据集将在以下网址提供：sompt22.github.io
Multi-object tracking (MOT) has been dominated by the use of track by detection approaches due to the success of convolutional neural networks (CNNs) on detection in the last decade. As the datasets and bench-marking sites are published, research direction has shifted towards yielding best accuracy on generic scenarios including re-identification (reID) of objects while tracking. In this study, we narrow the scope of MOT for surveillance by providing a dedicated dataset of pedestrians and focus on in-depth analyses of well performing multi-object trackers to observe the weak and strong sides of state-of-the-art (SOTA) techniques for real-world applications. For this purpose, we introduce SOMPT22 dataset; a new set for multi person tracking with annotated short videos captured from static cameras located on poles with 6-8 meters in height positioned for city surveillance. This provides a more focused and specific benchmarking of MOT for outdoor surveillance compared to public MOT datasets. We analyze MOT trackers classified as one-shot and two-stage with respect to the way of use of detection and reID networks on this new dataset. The experimental results of our new dataset indicate that SOTA is still far from high efficiency, and single-shot trackers are good candidates to unify fast execution and accuracy with competitive performance. The dataset will be available at: sompt22.github.io
