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Moving Object Detection Through Robust Matrix Completion Augmented With Objectness

Published on Dec 1, 2018in IEEE Journal of Selected Topics in Signal Processing6.69
· DOI :10.1109/JSTSP.2018.2869111
Behnaz Rezaei1
Estimated H-index: 1
(NU: Northeastern University),
Sarah Ostadabbas10
Estimated H-index: 10
(NU: Northeastern University)
Cite
Abstract
We present a novel approach for unsupervised detection of moving objects with nonsalient movements (e.g., rodents in their home cage). The proposed approach starts with separating the moving object from its background by modeling the background in a computationally efficient way. The background modeling is based on the assumption that background in natural videos lies on a low-dimensional subspace. We formulated and solved this problem using a low-rank matrix completion framework. To achieve computational efficiency, we proposed the fast robust matrix completion (fRMC) algorithm, which benefits from the in-face extended Frank–Wolfe approach as its optimization solver. We then augmented our fRMC-based moving object detection by incorporating the spatial information of the object as its objectness into the detection algorithm. With this augmentation we tackle the problem of nonsalient motion. The proposed fRMC algorithm is evaluated on background models challenge and Stuttgart artificial background subtraction datasets. Its detection results are then compared with the popular methods of background subtraction based on the robust principle component analysis and low-rank robust matrix completion methods, solved by inexact augmented Lagrangian multiplier and fast principal component pursuit via alternating minimization (FPCP). The outcomes showed faster computation, at least twice as when other methods are applied, while having a comparable detection accuracy. Moreover, fRMC observed to outperform the FPCP algorithm in background/foreground separation with minor computational overhead. Beyond that, we verified the performance improvement of the augmented fRMC with objectness on detecting the nonsalient motion of in-cage mice using the Caltech resident-intruder mice dataset. The evaluation showed 10% improvement in the detection performance, while significantly dropping the computational time.
  • References (58)
  • Citations (3)
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References58
Newest
Published on Oct 1, 2018in Medical & Biological Engineering & Computing2.04
Zheyuan Wang3
Estimated H-index: 3
(Georgia Institute of Technology),
S. Abdollah Mirbozorgi4
Estimated H-index: 4
(Georgia Institute of Technology),
Maysam Ghovanloo35
Estimated H-index: 35
(Georgia Institute of Technology)
A rodent behavior analysis system is presented, capable of automated tracking, pose estimation, and recognition of nine behaviors in freely moving animals. The system tracks three key points on the rodent body (nose, center of body, and base of tail) to estimate its pose and head rotation angle in real time. A support vector machine (SVM)-based model, including label optimization steps, is trained to classify on a frame-by-frame basis: resting, walking, bending, grooming, sniffing, rearing suppo...
Sajid Javed10
Estimated H-index: 10
(KNU: Kyungpook National University),
Arif Mahmood14
Estimated H-index: 14
(Qatar University)
+ 1 AuthorsSoon Ki Jung14
Estimated H-index: 14
(KNU: Kyungpook National University)
Background modeling constitutes the building block of many computer-vision tasks. Traditional schemes model the background as a low rank matrix with corrupted entries. These schemes operate in batch mode and do not scale well with the data size. Moreover, without enforcing spatiotemporal information in the low-rank component, and because of occlusions by foreground objects and redundancy in video data, the design of a background initialization method robust against outliers is very challenging. ...
Siyang Li (SC: University of Southern California), Bryan Seybold4
Estimated H-index: 4
(Google)
+ -3 AuthorsC.-C. Jay Kuo44
Estimated H-index: 44
(SC: University of Southern California)
We propose a method for unsupervised video object segmentation by transferring the knowledge encapsulated in image-based instance embedding networks. The instance embedding network produces an embedding vector for each pixel that enables identifying all pixels belonging to the same object. Though trained on static images, the instance embeddings are stable over consecutive video frames, which allows us to link objects together over time. Thus, we adapt the instance networks trained on static ima...
Published on Jan 1, 2018in IEEE Transactions on Neural Networks11.68
Xi Peng12
Estimated H-index: 12
(Agency for Science, Technology and Research),
Canyi Lu16
Estimated H-index: 16
(NUS: National University of Singapore)
+ 1 AuthorsHuajin Tang21
Estimated H-index: 21
(Sichuan University)
A lot of works have shown that frobenius-norm-based representation (FNR) is competitive to sparse representation and nuclear-norm-based representation (NNR) in numerous tasks such as subspace clustering. Despite the success of FNR in experimental studies, less theoretical analysis is provided to understand its working mechanism. In this brief, we fill this gap by building the theoretical connections between FNR and NNR. More specially, we prove that: 1) when the dictionary can provide enough rep...
Xi Peng12
Estimated H-index: 12
(Agency for Science, Technology and Research),
Jiwen Lu37
Estimated H-index: 37
(THU: Tsinghua University)
+ 1 AuthorsRui Yan10
Estimated H-index: 10
(Sichuan University)
In this paper, we address two challenging problems in unsupervised subspace learning: 1) how to automatically identify the feature dimension of the learned subspace (i.e., automatic subspace learning) and 2) how to learn the underlying subspace in the presence of Gaussian noise (i.e., robust subspace learning). We show that these two problems can be simultaneously solved by proposing a new method [(called principal coefficients embedding (PCE)]. For a given data set $\mathbf {D}\boldsymbol {\in ...
Behnaz Rezaei1
Estimated H-index: 1
(NU: Northeastern University),
Sarah Ostadabbas10
Estimated H-index: 10
(NU: Northeastern University)
Background subtraction is the primary task of the majority of video inspection systems. The most important part of the background subtraction which is common among different algorithms is background modeling. In this regard, our paper addresses the problem of background modeling in a computationally efficient way, which is important for current eruption of "big data" processing coming from high resolution multi-channel videos. Our model is based on the assumption that background in natural image...
Jingchun Cheng3
Estimated H-index: 3
(THU: Tsinghua University),
Yi-Hsuan Tsai9
Estimated H-index: 9
(UCM: University of California, Merced)
+ -3 AuthorsMing-Hsuan Yang70
Estimated H-index: 70
(UC: University of California)
This paper proposes an end-to-end trainable network, SegFlow, for simultaneously predicting pixel-wise object segmentation and optical flow in videos. The proposed SegFlow has two branches where useful information of object segmentation and optical flow is propagated bidirectionally in a unified framework. The segmentation branch is based on a fully convolutional network, which has been proved effective in image segmentation task, and the optical flow branch takes advantage of the FlowNet model....
Published on Jul 1, 2017 in CVPR (Computer Vision and Pattern Recognition)
Sergi Caelles5
Estimated H-index: 5
(ETH Zurich),
Kevis-Kokitsi Maninis8
Estimated H-index: 8
(ETH Zurich)
+ 3 AuthorsLuc Van Gool98
Estimated H-index: 98
(ETH Zurich)
This paper tackles the task of semi-supervised video object segmentation, i.e., the separation of an object from the background in a video, given the mask of the first frame. We present One-Shot Video Object Segmentation (OSVOS), based on a fully-convolutional neural network architecture that is able to successively transfer generic semantic information, learned on ImageNet, to the task of foreground segmentation, and finally to learning the appearance of a single annotated object of the test se...
Published on Jul 1, 2017 in CVPR (Computer Vision and Pattern Recognition)
Federico Perazzi11
Estimated H-index: 11
(MPG: Max Planck Society),
Anna Khoreva9
Estimated H-index: 9
(MPG: Max Planck Society)
+ 2 AuthorsAlexander Sorkine-Hornung15
Estimated H-index: 15
(Disney Research)
Inspired by recent advances of deep learning in instance segmentation and object tracking, we introduce the concept of convnet-based guidance applied to video object segmentation. Our model proceeds on a per-frame basis, guided by the output of the previous frame towards the object of interest in the next frame. We demonstrate that highly accurate object segmentation in videos can be enabled by using a convolutional neural network (convnet) trained with static images only. The key component of o...
Cited By3
Newest
Thierry Bouwmans22
Estimated H-index: 22
(University of La Rochelle),
B. Garcia-Garcia
Computer vision applications based on videos often require the detection of moving objects in their first step. Background subtraction is then applied in order to separate the background and the foreground. In literature, background subtraction is surely among the most investigated field in computer vision providing a big amount of publications. Most of them concern the application of mathematical and machine learning models to be more robust to the challenges met in videos. However, the ultimat...
Amirreza Farnoosh1
Estimated H-index: 1
(NU: Northeastern University),
Behnaz Rezaei1
Estimated H-index: 1
(NU: Northeastern University),
Sarah Ostadabbas10
Estimated H-index: 10
(NU: Northeastern University)
This paper presents a novel unsupervised probabilistic model estimation of visual background in video sequences using a variational autoencoder framework. Due to the redundant nature of the backgrounds in surveillance videos, visual information of the background can be compressed into a low-dimensional subspace in the encoder part of the variational autoencoder, while the highly variant information of its moving foreground gets filtered throughout its encoding-decoding process. Our deep probabil...