  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.

Computer Vision and Pattern Recognition;Machine Learning