Motion Segmentation
54 papers with code • 4 benchmarks • 7 datasets
Motion Segmentation is an essential task in many applications in Computer Vision and Robotics, such as surveillance, action recognition and scene understanding. The classic way to state the problem is the following: given a set of feature points that are tracked through a sequence of images, the goal is to cluster those trajectories according to the different motions they belong to. It is assumed that the scene contains multiple objects that are moving rigidly and independently in 3D-space.
Benchmarks
These leaderboards are used to track progress in Motion Segmentation
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Hopkins155
ApolloScape
EV-IMO
KT3DMoSeg
KITTI'15 MSplus
Latest papers
Segmenting Moving Objects via an Object-Centric Layered Representation
The objective of this paper is a model that is able to discover, track and segment multiple moving objects in a video.
Discovering Objects that Can Move
zpbao/discovery_obj_move
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CVPR 2022
Our experiments demonstrate that, despite only capturing a small subset of the objects that move, this signal is enough to generalize to segment both moving and static instances of dynamic objects.
HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction
leolyliu/HOI4D-Instructions
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CVPR 2022
We present HOI4D, a large-scale 4D egocentric dataset with rich annotations, to catalyze the research of category-level human-object interaction.
Self-Supervised Scene Flow Estimation with 4-D Automotive Radar
toytiny/cmflow
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Scene flow allows autonomous vehicles to reason about the arbitrary motion of multiple independent objects which is the key to long-term mobile autonomy.
EM-driven unsupervised learning for efficient motion segmentation
Etienne-Meunier-Inria/EM-Flow-Segmentation
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The core idea of our work is to leverage the Expectation-Maximization (EM) framework in order to design in a well-founded manner a loss function and a training procedure of our motion segmentation neural network that does not require either ground-truth or manual annotation.
Formulating Event-based Image Reconstruction as a Linear Inverse Problem with Deep Regularization using Optical Flow
tub-rip/event_based_image_rec_inverse_problem
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Event cameras are novel bio-inspired sensors that measure per-pixel brightness differences asynchronously.
Monocular Arbitrary Moving Object Discovery and Segmentation
michalneoral/Raptor
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The 32nd British Machine Vision Conference 2021
We propose a method for discovery and segmentation of objects that are, or their parts are, independently moving in the scene.
Unsupervised Object Learning via Common Fate
mtangemann/common_fate_object_learning
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Learning generative object models from unlabelled videos is a long standing problem and required for causal scene modeling.
Graph Constrained Data Representation Learning for Human Motion Segmentation
mdimiccoli/gcrl-for-hms
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ICCV 2021
Recently, transfer subspace learning based approaches have shown to be a valid alternative to unsupervised subspace clustering and temporal data clustering for human motion segmentation (HMS).
On Matrix Factorizations in Subspace Clustering
This article explores subspace clustering algorithms using CUR decompositions, and examines the effect of various hyperparameters in these algorithms on clustering performance on two real-world benchmark datasets, the Hopkins155 motion segmentation dataset and the Yale face dataset.
