Group Regression for Query Based Object Detection and Tracking
Felicia Ruppel Florian Faion Claudius Gläser Klaus Dietmayer
Group regression is commonly used in 3D object detection to predict box
parameters of similar classes in a joint head, aiming to benefit from
similarities while separating highly dissimilar classes. For query-based
perception methods, this has, so far, not been feasible. We close this gap and
present a method to incorporate multi-class group regression, especially
designed for the 3D domain in the context of autonomous driving, into existing
attention and query-based perception approaches. We enhance a transformer based
joint object detection and tracking model with this approach, and thoroughly
evaluate its behavior and performance. For group regression, the classes of the
nuScenes dataset are divided into six groups of similar shape and prevalence,
each being regressed by a dedicated head. We show that the proposed method is
applicable to many existing transformer based perception approaches and can
bring potential benefits. The behavior of query group regression is thoroughly
analyzed in comparison to a unified regression head, e.g. in terms of
class-switching behavior and distribution of the output parameters. The
proposed method offers many possibilities for further research, such as in the
direction of deep multi-hypotheses tracking.