Motion Segmentation with Weak Labeling Priors

Rahmati, Hodjat; Dragon, Ralf; Aamo, Ole Morten; Gool, Luc J. Van; Adde, Lars

doi:10.1007/978-3-319-11752-2_13

Cited by 14 publications

(11 citation statements)

References 21 publications

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“…A large number of motion segmentation approaches have been proposed, including [3,[6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. The prior literature is too large to review here, so we focus on recent methods.…”

Section: Related Workmentioning

confidence: 99%

“…We modify the standard RANSAC procedure to force the algorithm to choose three of the 10 patches from the image corners, because image corners are prone to errors due to a misestimated camera rotation. Since the Bruss and Horn error function (Equation 15) does not penalize motions in a direction opposite of the predicted motion, we modify it to penalize these motions appropriately (details in Supp. Mat.).…”

Section: Initialization: Segmenting the First Framementioning

confidence: 99%

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It’s Moving! A Probabilistic Model for Causal Motion Segmentation in Moving Camera Videos

Bideau

Learned-Miller

2016

Lecture Notes in Computer Science

102

143

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Abstract. The human ability to detect and segment moving objects works in the presence of multiple objects, complex background geometry, motion of the observer, and even camouflage. In addition to all of this, the ability to detect motion is nearly instantaneous. While there has been much recent progress in motion segmentation, it still appears we are far from human capabilities. In this work, we derive from first principles a new likelihood function for assessing the probability of an optical flow vector given the 3D motion direction of an object. This likelihood uses a novel combination of the angle and magnitude of the optical flow to maximize the information about the true motions of objects. Using this new likelihood and several innovations in initialization, we develop a motion segmentation algorithm that beats current state-of-the-art methods by a large margin. We compare to five state-of-the-art methods on two established benchmarks, and a third new data set of camouflaged animals, which we introduce to push motion segmentation to the next level.

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Section: Related Workmentioning

confidence: 99%

Section: Initialization: Segmenting the First Framementioning

confidence: 99%

It’s Moving! A Probabilistic Model for Causal Motion Segmentation in Moving Camera Videos

Bideau

Learned-Miller

2016

Lecture Notes in Computer Science

102

143

View full text Add to dashboard Cite

show abstract

“…In clinical observations, CS and FM are early markers for later development of CP [ 11 , 92 ]. Therefore, to get a good feature set that represents the full clinical insight, the authors in [ 32 ] implemented a motion segmentation method proposed in [ 93 ]. They collected a dataset of 78 infants recorded with a 2D monocular camera.…”

Section: Methodology Of the Reviewed Approachesmentioning

confidence: 99%

AI Approaches towards Prechtl’s Assessment of General Movements: A Systematic Literature Review

Irshad

Nisar

Gouverneur

et al. 2020

Sensors

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General movements (GMs) are spontaneous movements of infants up to five months post-term involving the whole body varying in sequence, speed, and amplitude. The assessment of GMs has shown its importance for identifying infants at risk for neuromotor deficits, especially for the detection of cerebral palsy. As the assessment is based on videos of the infant that are rated by trained professionals, the method is time-consuming and expensive. Therefore, approaches based on Artificial Intelligence have gained significantly increased attention in the last years. In this article, we systematically analyze and discuss the main design features of all existing technological approaches seeking to transfer the Prechtl’s assessment of general movements from an individual visual perception to computer-based analysis. After identifying their shared shortcomings, we explain the methodological reasons for their limited practical performance and classification rates. As a conclusion of our literature study, we conceptually propose a methodological solution to the defined problem based on the groundbreaking innovation in the area of Deep Learning.

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“…The remaining problem is how to solve the optimization problem in Eq.1 to partition the graph into the optimal number of segments. A natural optimization approach would be applying spectral clustering [28] or its recent variants such as multi-label graph-cut [29] or unbalanced energy [30]. Although these methods can easily specify which trajectories should belong to the same segment, they do not specify which should be separated.…”

Section: Graph Partitioningmentioning

confidence: 99%

Mining Minimal Map-Segments for Visual Place Classifiers

Kanji¹

2019

Preprint

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In visual place recognition (VPR), map segmentation (MS) is a preprocessing technique used to partition a given view-sequence map into place classes (i.e., map segments) so that each class has good place-specific training images for a visual place classifier (VPC). Existing approaches to MS implicitly/explicitly suppose that map segments have a certain size, or individual map segments are balanced in size. However, recent VPR systems showed that very small important map segments (minimal map segments) often suffice for VPC, and the remaining large unimportant portion of the map should be discarded to minimize map maintenance cost. Here, a new MS algorithm that can mine minimal map segments from a large view-sequence map is presented. To solve the inherently NP hard problem, MS is formulated as a video-segmentation problem and the efficient point-trajectory based paradigm of video segmentation is used. The proposed map representation was implemented with three types of VPC: deep convolutional neural network, bag-of-words, and object class detector, and each was integrated into a Monte Carlo localization algorithm (MCL) within a topometric VPR framework. Experiments using the publicly available NCLT dataset thoroughly investigate the efficacy of MS in terms of VPR performance.

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Motion Segmentation with Weak Labeling Priors

Cited by 14 publications

References 21 publications

It’s Moving! A Probabilistic Model for Causal Motion Segmentation in Moving Camera Videos

It’s Moving! A Probabilistic Model for Causal Motion Segmentation in Moving Camera Videos

AI Approaches towards Prechtl’s Assessment of General Movements: A Systematic Literature Review

Mining Minimal Map-Segments for Visual Place Classifiers

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