Abstract
Tracking objects in three-dimensional space is critical for autonomous driving. To ensure safety while driving, the tracker must be able to reliably track objects across frames and accurately estimate their states such as velocity and acceleration in the present. Existing works frequently focus on the association task while either neglecting the model performance on state estimation or deploying complex heuristics to predict the states. In this paper, we propose STT, a Stateful Tracking model built with Transformers, that can consistently track objects in the scenes while also predicting their states accurately. STT consumes rich appearance, geometry, and motion signals through long term history of detections and is jointly optimized for both data association and state estimation tasks. Since the standard tracking metrics like MOTA and MOTP do not capture the combined performance of the two tasks in the wider spectrum of object states, we extend them with new metrics called S-MOTA and MOTPS that address this limitation. STT achieves competitive real-time performance on the Waymo Open Dataset.
Abstract (translated)
在自动驾驶中，在三维空间中跟踪物体至关重要。为了在驾驶过程中确保安全，跟踪器必须能够可靠地跟踪物体跨越帧并准确估计其状态，如速度和加速度。现有作品经常关注关联任务，或者忽略了对状态估计的模型性能，或者采用复杂的策略预测状态。在本文中，我们提出了一种名为STT的基于Transformer的状态ful跟踪模型，可以在场景中持续跟踪物体并准确预测其状态。STT通过检测检测器的长期历史来消耗丰富的外观、几何和运动信号，并针对数据关联和状态估计任务与数据进行联合优化。由于标准跟踪指标（如MOTA和MOTP）没有涵盖两个任务在更广泛的物体状态范围内的综合性能，我们扩展了它们，并引入了名为S-MOTA和MOTPS的新指标，解决了这一局限。STT在Waymo Open Dataset上实现了与竞争者相当实时性能。
URL
https://arxiv.org/abs/2405.00236
