Search Results for author:
Found 3 papers, 3 papers with code
CLRmatchNet: Enhancing Curved Lane Detection with Deep Matching Process
Our research introduces MatchNet, a deep learning submodule-based approach aimed at improving the label assignment process.
Ranked #11 on
Lane Detection
on CULane
Strong-TransCenter: Improved Multi-Object Tracking based on Transformers with Dense Representations
TransCenter is the first center-based transformer framework for MOT, and is also among the first to show the benefits of using transformer-based architectures for MOT.
Ranked #1 on
Multiple Object Tracking with Transformer
on MOT20
Multi-Object Tracking
Multiple Object Tracking with Transformer
BoT-SORT: Robust Associations Multi-Pedestrian Tracking
The goal of multi-object tracking (MOT) is detecting and tracking all the objects in a scene, while keeping a unique identifier for each object.
Ranked #3 on
Multi-Object Tracking
on MOT20
(using extra training data)
