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 with no code
Neuromorphic Vision-based Motion Segmentation with Graph Transformer Neural Network
Moreover, we introduce the Dynamic Object Mask-aware Event Labeling (DOMEL) approach for generating approximate ground-truth labels for event-based motion segmentation datasets.
Out of the Room: Generalizing Event-Based Dynamic Motion Segmentation for Complex Scenes
Rapid and reliable identification of dynamic scene parts, also known as motion segmentation, is a key challenge for mobile sensors.
A Unified Model Selection Technique for Spectral Clustering Based Motion Segmentation
Motion segmentation is a fundamental problem in computer vision and is crucial in various applications such as robotics, autonomous driving and action recognition.
WoodScape Motion Segmentation for Autonomous Driving -- CVPR 2023 OmniCV Workshop Challenge
Motion segmentation is a complex yet indispensable task in autonomous driving.
Appearance-based Refinement for Object-Centric Motion Segmentation
The goal of this paper is to discover, segment, and track independently moving objects in complex visual scenes.
Un-EvMoSeg: Unsupervised Event-based Independent Motion Segmentation
Event cameras are a novel type of biologically inspired vision sensor known for their high temporal resolution, high dynamic range, and low power consumption.
Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenes
To resolve this issue, we introduce Dynamo-Depth, an unifying approach that disambiguates dynamical motion by jointly learning monocular depth, 3D independent flow field, and motion segmentation from unlabeled monocular videos.
Segmenting the motion components of a video: A long-term unsupervised model
Human beings have the ability to continuously analyze a video and immediately extract the motion components.
Motion Segmentation from a Moving Monocular Camera
We then construct two robust affinity matrices representing the pairwise object motion affinities throughout the whole video using epipolar geometry and the motion information provided by optical flow.
Joint Self-supervised Depth and Optical Flow Estimation towards Dynamic Objects
In this work, we construct a joint inter-frame-supervised depth and optical flow estimation framework, which predicts depths in various motions by minimizing pixel wrap errors in bilateral photometric re-projections and optical vectors.
