Multi-object tracking (MOT) is among crucial applications in modern advanced driver
assistance systems (ADAS) and autonomous driving (AD) systems. The global nearest …

Abstract Multiple Object Tracking (MOT) is crucial to autonomous vehicle perception. End-to-
end transformer-based algorithms, which detect and track objects simultaneously, show …

Following the tracking-by-attention paradigm, this letter introduces an object-centric,
transformer-based framework for tracking in 3D. Traditional model-based tracking …

In the multi-object tracking (MOT) algorithm based on the tracking-by-detection paradigm,
matching accuracy between detection and prediction, robustness to occlusion and …

Online multi-object tracking (MOT) plays a pivotal role in autonomous systems. The state-of-
the-art approaches usually employ a tracking-by-detection method, and data association …

Pose prediction and trajectory forecasting represent pivotal tasks in the realm of
autonomous driving, crucially enhancing the planning and decision-making capabilities of …

The attention mechanism has produced impressive results in object tracking, but for a good
trade‐off between performance and efficiency, CNN‐based approaches still dominate …

3D object detection models that exploit both LiDAR and camera sensor features are top
performers in large-scale autonomous driving benchmarks. A transformer is a popular …

Tracking 3D objects is an important task in autonomous driving. Classical Kalman Filtering
based methods are still the most popular solutions. However, these methods require …

We present TransLPC, a novel detection model for large point clouds that is based on a
transformer architecture. While object detection with transformers has been an active field of …