There has been significant progress on pose estimation and increasing
interests on pose tracking in recent years. At the same time, the overall
algorithm and system complexity increases as well, making the algorithm
analysis and comparison more difficult. This work provides simple and effective
baseline methods. They are helpful for inspiring and evaluating new ideas for
the field. State-of-the-art results are achieved on challenging benchmarks. The
code will be available at this https URL

该研究提供了简单有效的基准方法，对于启发和评估领域中的新想法非常有帮助，并在具有挑战性的基准测试中取得了最先进的结果。

用于人体姿态估计和跟踪的简单基线

Simple Baselines for Human Pose Estimation and Tracking

Multi-object tracking has recently become an important area of computer
vision, especially for Advanced Driver Assistance Systems (ADAS). Despite
growing attention, achieving high performance tracking is still challenging,
with state-of-the- art systems resulting in high complexity with a large number
of hyper parameters. In this paper, we focus on reducing overall system
complexity and the number hyper parameters that need to be tuned to a specific
environment. We introduce a novel tracking system based on similarity mapping
by Enhanced Siamese Neural Network (ESNN), which accounts for both appearance
and geometric information, and is trainable end-to-end. Our system achieves
competitive performance in both speed and accuracy on MOT16 challenge, compared
to known state-of-the-art methods.

本文介绍了一种基于增强 Siamese 神经网络的相似性映射的跟踪系统，该系统考虑了外观和几何信息，并可进行端到端的训练，以减少整体系统复杂性和需要针对特定环境进行调整的超参数数量，在 MOT16 挑战中取得了具有竞争性的速度和准确性。