We tackle the problem of joint perception and motion forecasting in the context of self-driving
vehicles. Towards this goal we propose PnPNet, an end-to-end model that takes as input …

Utilizing infrastructure and vehicle-side information to track and forecast the behaviors of
surrounding traffic participants can significantly improve decision-making and safety in …

Vehicle tracking is an essential task in the multi-object tracking (MOT) field. A distinct
characteristic in vehicle tracking is that the trajectories of vehicles are fairly smooth in both …

In this paper, we study masked autoencoder (MAE) pretraining on videos for matching-
based downstream tasks, including visual object tracking (VOT) and video object …

The recent trend in multiple object tracking (MOT) is heading towards leveraging deep
learning to boost the tracking performance. However, it is not trivial to solve the data …

We present Argoverse, a dataset designed to support autonomous vehicle perception tasks
including 3D tracking and motion forecasting. Argoverse includes sensor data collected by a …

Anomaly detection on road traffic is a fundamental computer vision task and plays a critical
role in video structure analysis and urban traffic analysis. Although it has attracted intense …

We present a novel model for Tracking Any Point (TAP) that effectively tracks any queried
point on any physical surface throughout a video sequence. Our approach employs two …

The large-scale deployment of autonomous vehicles is yet to come, and one of the major
remaining challenges lies in urban dense traffic scenarios. In such cases, it remains …

We present RobustTP, an end-to-end algorithm for predicting future trajectories of road-
agents in dense traffic with noisy sensor input trajectories obtained from RGB cameras …