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
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.
LiDAR-BEVMTN: Real-Time LiDAR Bird's-Eye View Multi-Task Perception Network for Autonomous Driving
We achieve state-of-the-art results for two tasks, semantic and motion segmentation, and close to state-of-the-art performance for 3D object detection.
Divided Attention: Unsupervised Multi-Object Discovery with Contextually Separated Slots
We introduce a method to segment the visual field into independently moving regions, trained with no ground truth or supervision.
Segmentation Guided Deep HDR Deghosting
Our motion segmentation guided HDR fusion approach offers significant advantages over existing HDR deghosting methods.
Self-Supervised Deep Subspace Clustering with Entropy-norm
The local structure and dense connectivity of the original data benefit from the self-expressive layer and additional entropy-norm constraint.
EVIMO2: An Event Camera Dataset for Motion Segmentation, Optical Flow, Structure from Motion, and Visual Inertial Odometry in Indoor Scenes with Monocular or Stereo Algorithms
Depth and segmentation are provided at 60 Hz for the event cameras and 30 Hz for the classical camera.
Quantum Motion Segmentation
Motion segmentation is a challenging problem that seeks to identify independent motions in two or several input images.
ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation
Dynamic Vision Sensor (DVS) can asynchronously output the events reflecting apparent motion of objects with microsecond resolution, and shows great application potential in monitoring and other fields.
The Right Spin: Learning Object Motion from Rotation-Compensated Flow Fields
In this work, we argue that the coupling of camera rotation and camera translation can create complex motion fields that are difficult for a deep network to untangle directly.
Consistency and Diversity induced Human Motion Segmentation
Besides, a novel constraint based on the Hilbert Schmidt Independence Criterion (HSIC) is introduced to ensure the diversity of multi-level subspace representations, which enables the complementarity of multi-level representations to be explored to boost the transfer learning performance.
