Advancements in tracking algorithms have empowered nascent applications across various domains, from steering autonomous vehicles to guiding robots to enhancing augmented reality experiences for users. However, these algorithms are application-specific and do not work across applications with different types of motion; even a tracking algorithm designed for a given application does not work in scenarios deviating from highly standard conditions. For example, a tracking algorithm designed for robot navigation inside a building will not work for tracking the same robot in an outdoor environment. To demonstrate this problem, we evaluate the performance of the state-of-the-art tracking methods across various applications and scenarios. To inform our analysis, we first categorize algorithmic, environmental, and locomotion-related challenges faced by tracking algorithms. We quantitatively evaluate the performance using multiple tracking algorithms and representative datasets for a wide range of Internet of Things (IoT) and Extended Reality (XR) applications, including autonomous vehicles, drones, and humans. Our analysis shows that no tracking algorithm works across different applications and scenarios within applications. Ultimately, using the insights generated from our analysis, we discuss multiple approaches to improving the tracking performance using input data characterization, leveraging intermediate information, and output evaluation.

翻译：追踪算法的进步推动了多个领域新兴应用的发展，从自动驾驶车辆的操控到机器人导航，再到增强用户体验的增强现实应用。然而，这些算法通常针对特定应用设计，无法适应具有不同运动模式的应用场景；即便是为特定应用设计的追踪算法，在偏离高度标准化条件的环境下也会失效。例如，为室内机器人导航设计的追踪算法无法用于追踪同一机器人在户外环境中的运动。为揭示这一问题，我们评估了当前最先进的追踪方法在多种应用与场景下的性能。为支撑分析，我们首先对追踪算法面临的算法层面、环境层面及运动相关的挑战进行了分类。通过采用多种追踪算法和代表性数据集，我们对广泛的物联网（IoT）与扩展现实（XR）应用（包括自动驾驶车辆、无人机及人体运动追踪）进行了定量性能评估。我们的分析表明，目前尚无任何一种追踪算法能够跨不同应用及同一应用内的不同场景稳定工作。最后，基于分析所得的见解，我们探讨了通过输入数据特征化、利用中间信息以及输出评估等多种途径提升追踪性能的可能方向。