论文标题
用于点云上多对象跟踪的变压器
Transformers for Multi-Object Tracking on Point Clouds
论文作者
论文摘要
我们提出了TransMot，这是一种基于变压器的新型端到端可训练的在线跟踪器和点云数据的检测器。该模型利用了跨界和自我注意的机制，适用于汽车环境中的激光雷达数据以及其他数据类型，例如雷达。轨道管理和新轨道的检测均由相同的变压器解码器模块执行，并且跟踪器状态在特征空间中编码。通过这种方法，我们利用检测器的丰富潜在空间进行跟踪，而不是依靠低维度的边界框。尽管如此，我们仍然能够保留传统基于卡尔曼过滤器的方法的某些理想属性，例如在任意时间步中处理传感器输入或补偿帧跳过的能力。由于一个新颖的模块，可以将轨道信息从一个帧转换为特征级别的下一个帧，从而完成类似的任务，从而将轨道信息从一个帧转换为一个类似的任务，这是可以将轨道信息从一个帧转换为一个与卡尔曼过滤器的预测步骤相似的，这是可能的。结果显示在具有挑战性的现实数据集Nuscenes上，其中所提出的模型的表现优于其基于Kalman滤镜的跟踪基线。
We present TransMOT, a novel transformer-based end-to-end trainable online tracker and detector for point cloud data. The model utilizes a cross- and a self-attention mechanism and is applicable to lidar data in an automotive context, as well as other data types, such as radar. Both track management and the detection of new tracks are performed by the same transformer decoder module and the tracker state is encoded in feature space. With this approach, we make use of the rich latent space of the detector for tracking rather than relying on low-dimensional bounding boxes. Still, we are able to retain some of the desirable properties of traditional Kalman-filter based approaches, such as an ability to handle sensor input at arbitrary timesteps or to compensate frame skips. This is possible due to a novel module that transforms the track information from one frame to the next on feature-level and thereby fulfills a similar task as the prediction step of a Kalman filter. Results are presented on the challenging real-world dataset nuScenes, where the proposed model outperforms its Kalman filter-based tracking baseline.
