3 research outputs found
Towards Segmenting Anything That Moves
Detecting and segmenting individual objects, regardless of their category, is
crucial for many applications such as action detection or robotic interaction.
While this problem has been well-studied under the classic formulation of
spatio-temporal grouping, state-of-the-art approaches do not make use of
learning-based methods. To bridge this gap, we propose a simple learning-based
approach for spatio-temporal grouping. Our approach leverages motion cues from
optical flow as a bottom-up signal for separating objects from each other.
Motion cues are then combined with appearance cues that provide a generic
objectness prior for capturing the full extent of objects. We show that our
approach outperforms all prior work on the benchmark FBMS dataset. One
potential worry with learning-based methods is that they might overfit to the
particular type of objects that they have been trained on. 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.Comment: Website: http://www.achaldave.com/projects/anything-that-moves/.
Code: https://github.com/achalddave/segment-any-movin
Betrayed by Motion: Camouflaged Object Discovery via Motion Segmentation
The objective of this paper is to design a computational architecture that
discovers camouflaged objects in videos, specifically by exploiting motion
information to perform object segmentation. We make the following three
contributions: (i) We propose a novel architecture that consists of two
essential components for breaking camouflage, namely, a differentiable
registration module to align consecutive frames based on the background, which
effectively emphasises the object boundary in the difference image, and a
motion segmentation module with memory that discovers the moving objects, while
maintaining the object permanence even when motion is absent at some point.
(ii) We collect the first large-scale Moving Camouflaged Animals (MoCA) video
dataset, which consists of over 140 clips across a diverse range of animals (67
categories). (iii) We demonstrate the effectiveness of the proposed model on
MoCA, and achieve competitive performance on the unsupervised segmentation
protocol on DAVIS2016 by only relying on motion.Comment: ACCV 202
Object Discovery in Videos as Foreground Motion Clustering
We consider the problem of providing dense segmentation masks for object
discovery in videos. We formulate the object discovery problem as foreground
motion clustering, where the goal is to cluster foreground pixels in videos
into different objects. We introduce a novel pixel-trajectory recurrent neural
network that learns feature embeddings of foreground pixel trajectories linked
across time. By clustering the pixel trajectories using the learned feature
embeddings, our method establishes correspondences between foreground object
masks across video frames. To demonstrate the effectiveness of our framework
for object discovery, we conduct experiments on commonly used datasets for
motion segmentation, where we achieve state-of-the-art performance