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
Most implemented papers
Multi-Class Model Fitting by Energy Minimization and Mode-Seeking
The move replaces a set of labels with the corresponding density mode in the model parameter domain, thus achieving fast and robust optimization.
Adaptive Low-Rank Kernel Subspace Clustering
In this paper, we present a kernel subspace clustering method that can handle non-linear models.
Motion Segmentation by Exploiting Complementary Geometric Models
Many real-world sequences cannot be conveniently categorized as general or degenerate; in such cases, imposing a false dichotomy in using the fundamental matrix or homography model for motion segmentation would lead to difficulty.
Motion-based Object Segmentation based on Dense RGB-D Scene Flow
stanford-iprl-lab/sceneflownet
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Our model jointly estimates (i) the segmentation of the scene into an unknown but finite number of objects, (ii) the motion trajectories of these objects and (iii) the object scene flow.
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation
We address the unsupervised learning of several interconnected problems in low-level vision: single view depth prediction, camera motion estimation, optical flow, and segmentation of a video into the static scene and moving regions.
Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimation
FilippoAleotti/Dwarf-Tensorflow
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ECCV 2018
Making use of the estimated occlusions, we also show improved results on motion segmentation and scene flow estimation.
Extending Layered Models to 3D Motion
We consider the problem of inferring a layered representa-tion, its depth ordering and motion segmentation from a video in whichobjects may undergo 3D non-planar motion relative to the camera.
Towards Segmenting Anything That Moves
achalddave/segment-any-moving
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To address this concern, we propose two new benchmarks for generic, moving object detection, and show that our model matches top-down methods on common categories, while significantly out-performing both top-down and bottom-up methods on never-before-seen categories.
Event-Based Motion Segmentation by Motion Compensation
In contrast to traditional cameras, whose pixels have a common exposure time, event-based cameras are novel bio-inspired sensors whose pixels work independently and asynchronously output intensity changes (called "events"), with microsecond resolution.
Robust Motion Segmentation from Pairwise Matches
In this paper we address a classification problem that has not been considered before, namely motion segmentation given pairwise matches only.
