A piece of orbital debris smaller than a marble can punch through a working satellite at speeds exceeding 10 kilometers per ...

The NSF-DOE Vera C. Rubin Observatory has cataloged more than 11,000 new asteroids during its first weeks of pre-survey ...

Understanding the world in terms of objects and the possible interactions with them is an important cognitive ability. However, current world models adopted in reinforcement learning typically lack ...

Self-driving cars can’t afford mistakes. Missing a traffic light or a pedestrian could mean disaster. But object detection in dynamic urban environments? That’s hard. I worked on optimizing object ...

We implemented Panopticus using Python and CUDA for GPU-based acceleration. All neural networks were developed using PyTorch [41] and trained on the training set in the nuScenes dataset [2]. Note that ...

Abstract: Multiple Object Tracking (MOT) is a rapidly developing research field that targets precise and reliable tracking of objects. Unfortunately, most available MOT datasets typically contain ...

Abstract: Tracking objects in soccer videos is extremely important to gather both player and team statistics, whether it is to estimate the total distance run, the ball possession or the team ...

This repository is an official implementation of the paper MOTR: End-to-End Multiple-Object Tracking with TRansformer. TL; DR. MOTR is a fully end-to-end multiple-object tracking framework based on ...

This repository largely implements the approach described in Learning a Neural Solver for Multiple Object Tracking. This implementation achieves ~58% MOTA on the ...