2 research outputs found
Multi-Object Tracking and Segmentation via Neural Message Passing
Graphs offer a natural way to formulate Multiple Object Tracking (MOT) and
Multiple Object Tracking and Segmentation (MOTS) within the
tracking-by-detection paradigm. However, they also introduce a major challenge
for learning methods, as defining a model that can operate on such structured
domain is not trivial. In this work, we exploit the classical network flow
formulation of MOT to define a fully differentiable framework based on Message
Passing Networks (MPNs). By operating directly on the graph domain, our method
can reason globally over an entire set of detections and exploit contextual
features. It then jointly predicts both final solutions for the data
association problem and segmentation masks for all objects in the scene while
exploiting synergies between the two tasks. We achieve state-of-the-art results
for both tracking and segmentation in several publicly available datasets. Our
code is available at github.com/ocetintas/MPNTrackSeg.Comment: arXiv admin note: substantial text overlap with arXiv:1912.0751