Video background modeling: recent approaches, issues and our proposed techniques

Shah, Munir; Deng, Jeremiah D.; Woodford, Brendon J.

doi:10.1007/s00138-013-0552-7

Cited by 66 publications

(25 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Background-foreground separation in a video taken by a static camera is a crucial step for detecting moving objects in the video surveillance systems [25], [26], [29]. Before the work of Candès in 2009, this task was usually addressed by statistical modeling [244], [251], [260], fuzzy modeling [15], [16], [17], [27] and conventional subspace learning model either reconstructive [65], [66], [162], [239], [207], [253], [282], [321] and discriminative [84], [85], [192]. However, RPCA methods immediately provided a very promising solution towards moving object detection.…”

Section: A Background-foreground Separationmentioning

confidence: 99%

On the Applications of Robust PCA in Image and Video Processing

et al. 2018

View full text Add to dashboard Cite

Robust PCA (RPCA) via decomposition into lowrank plus sparse matrices offers a powerful framework for a large variety of applications such as image processing, video processing and 3D computer vision. Indeed, most of the time these applications require to detect sparse outliers from the observed imagery data that can be approximated by a lowrank matrix. Moreover, most of the time experiments show that RPCA with additional spatial and/or temporal constraints often outperforms the state-of-the-art algorithms in these applications. Thus, the aim of this paper is to survey the applications of RPCA in computer vision. In the first part of this paper, we review representative image processing applications as follows: (1) lowlevel imaging such as image recovery and denoising, image composition, image colorization, image alignment and rectification, multi-focus image and face recognition, (2) medical imaging like dynamic Magnetic Resonance Imaging (MRI) for acceleration of data acquisition, background suppression and learning of inter-frame motion fields, and (3) imaging for 3D computer vision with additional depth information like in Structure from Motion (SfM) and 3D motion recovery. In the second part, we present the applications of RPCA in video processing which utilize additional spatial and temporal information compared to image processing. Specifically, we investigate video denoising and restoration, hyperspectral video and background/foreground separation. Finally, we provide perspectives on possible future research directions and algorithmic frameworks that are suitable for these applications.

show abstract

Section: A Background-foreground Separationmentioning

confidence: 99%

On the Applications of Robust PCA in Image and Video Processing

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Several video background modeling methods, using different approaches such as Gaussian mixture models [4], kernel density estimations [5], or neural networks [6], exist in the literature. More comprehensive surveys of other methods are presented in [1,7]. Principal component pursuit (PCP) is currently considered to be one of the leading algorithms for video background modeling [8].…”

Section: Introductionmentioning

confidence: 99%

Panning and Jitter Invariant Incremental Principal Component Pursuit for Video Background Modeling

Chau

Rodríguez

2019

Journal of Electrical and Computer Engineering

View full text Add to dashboard Cite

Video background modeling is an important preprocessing stage for various applications, and principal component pursuit (PCP) is among the state-of-the-art algorithms for this task. One of the main drawbacks of PCP is its sensitivity to jitter and camera movement. This problem has only been partially solved by a few methods devised for jitter or small transformations. However, such methods cannot handle the case of moving or panning cameras in an incremental fashion. In this paper, we greatly expand the results of our earlier work, in which we presented a novel, fully incremental PCP algorithm, named incPCP-PTI, which was able to cope with panning scenarios and jitter by continuously aligning the low-rank component to the current reference frame of the camera. To the best of our knowledge, incPCP-PTI is the first low-rank plus additive incremental matrix method capable of handling these scenarios in an incremental way. The results on synthetic videos and Moseg, DAVIS, and CDnet2014 datasets show that incPCP-PTI is able to maintain a good performance in the detection of moving objects even when panning and jitter are present in a video. Additionally, in most videos, incPCP-PTI obtains competitive or superior results compared to state-of-the-art batch methods.

show abstract

“…Moreover, it uses pixel space variation mix ratio to improve the classification accuracy rate. Shah et al [22] introduced a new local parameter learning algorithm for the Gaussian mixture mode background model, and used SURF feature matching algorithm to suppress the ghosts. Jie et al [23] presented a technique based on PCA and the Gaussian mixture model to segment color image of diseased wheat.…”

Section: Introductionmentioning

confidence: 99%

Individual pig object detection algorithm based on Gaussian mixture model

Sun

Yuanbing

et al. 2017

International Journal of Agricultural and Biological Engineering

View full text Add to dashboard Cite

The background models are crucially important for the object extraction for moving objects detection in a video. The Gaussian mixture model (GMM) is one of popular methods in the background models. Gaussian mixture model which applied to the pig target detection has some shortcomings such as low efficiency of algorithm, misjudgment points and ghosts. This study proposed an improved algorithm based on adaptive Gaussian mixture model, to overcome the deficiencies of the traditional Gaussian mixture model in pig object detection. Based on Gaussian mixture background model, this paper introduced two new parameters of video frames m and T 0. The Gaussian distribution was scanned once every m frames, the excessive Gaussian distribution was deleted to improve the convergence speed of the model. Meanwhile, using different learning rates to suppress ghosts, a higher decreasing learning rate was adopted to accelerate the background modeling before T 0 , the background model would become stable as the time continued and a smaller learning rate could be used. In order to maintain a stable background and reduce noise interference, a fixed learning rate after T 0 was used. Results of experiments indicated that this algorithm could quickly build the initial background model, detect the moving target pigs, and extract the complete contours of the target pigs'. The algorithm is characterized by good robustness and adaptability.

show abstract

Video background modeling: recent approaches, issues and our proposed techniques

Cited by 66 publications

References 36 publications

On the Applications of Robust PCA in Image and Video Processing

On the Applications of Robust PCA in Image and Video Processing

Panning and Jitter Invariant Incremental Principal Component Pursuit for Video Background Modeling

Individual pig object detection algorithm based on Gaussian mixture model

Contact Info

Product

Resources

About