Motion detection based on the combining of the background subtraction and the structure–texture decomposition

Elharrouss, Omar; Moujahid, Driss; Tairi, Hamid

doi:10.1016/j.ijleo.2015.08.084

Cited by 26 publications

(14 citation statements)

References 7 publications

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“…Background subtraction can be used for motion detection by separating background and foreground and then, by masking the binary color space, objects can be further classified as background and foreground [53]. The authors of Reference [54] propose an hierarchical data structure and background subtraction method of motion detection.…”

Section: Motion Detectionmentioning

confidence: 99%

Real-Time, Content-Based Communication Load Reduction in the Internet of Multimedia Things

et al. 2020

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There is an increasing number of devices available for the Internet of Multimedia Things (IoMT). The demands these ever-more complex devices make are also increasing in terms of energy efficiency, reliability, quality-of-service guarantees, higher data transfer rates, and general security. The IoMT itself faces challenges when processing and storing massive amounts of data, transmitting it over low bandwidths, bringing constrained resources to bear and keeping power consumption under check. This paper’s research focuses on an efficient video compression technique to reduce that communication load, potentially generated by diverse camera sensors, and also improve bit-rates, while ensuring accuracy of representation and completeness of video data. The proposed method applies a video content-based solution, which, depending on the motion present between consecutive frames, decides on whether to send only motion information or no frame information at all. The method is efficient in terms of limiting the data transmitted, potentially conserving device energy, and reducing latencies by means of negotiable processing overheads. Data are also encrypted in the interests of confidentiality. Video quality measurements, along with a good number of Quality-of-Service measurements demonstrated the value of the load reduction, as is also apparent from a comparison with other related methods.

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Section: Motion Detectionmentioning

confidence: 99%

Real-Time, Content-Based Communication Load Reduction in the Internet of Multimedia Things

et al. 2020

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“…Different methods are explored in this context. Most of them are classified as background subtraction methods [8][9][10][11][12], [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. Where, the Background Modeling is the principal step in these different methods.…”

Section: Related Workmentioning

confidence: 99%

“…The second approach is the calculation of the optical flow [3], [4] which provides all information about the movement, but the real-time implementation is difficult and calculation of flow being generally slow. The background subtraction approaches start by modeling the background(model of static scene), it can detect the most precise foreground [5][6] [ [24][25][26][27][28][29][30][31], , but it has many limitations like sensor noise (noise of acquisition and digitization) and management of homogeneous areas when the luminance difference between two moments is less than a threshold.…”

Section: Introductionmentioning

confidence: 99%

A block-based background model for moving object detection

Elharrouss

Abbad

Moujahid

et al. 2017

ELCVIA

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Detecting the moving objects in a video sequence using a stationary camera is an important task for many computer vision applications. This paper proposes a background subtraction approach. As first step, the background is initialized using the block-based analysis before being updated in each incoming frame. Our background frame is generated by collecting the blocks background candidates. The block candidate selection is based on probability density function (pdf) computation. After that, we compute the absolute difference between the background frame and each frame of sequence. A noise filter is applied using the Structure/Texture decomposition in order to minimize the noise caused by background subtraction operation. The binary motion mask is formed using an adaptive threshold that was deduced from the weighted mean and variance calculation. To assure the correspondence between the current frame and the background frame, an adaptation of background model in each incoming frame is realized. After comparing results obtained from the proposed method to other existing ones, it was shown that our approach attains a higher degree of efficacy.Key Words: Motion detection, Background subtraction, Background model, Background update, Video surveillance. IntroductionMotion detection is a paramount study field of computer vision. Its purpose is to extract the moving objects at time t in a video captured using a stationary camera. The motion detection is used for many applications. Among these applications there are video surveillance, human-machine interaction, the recognition of sign language specific to robotics applications and many others.The approaches mainly used for motion detection can be classified into three categories: the time difference methods, the analysis of optical flow, and the background subtraction methods. For the first one, the time difference being the calculation of the difference between two or more consecutive images in order to extract the moving area [1], [2], but the problem in this approach is that the detected objects are incomplete and poorly presented. The second approach is the calculation of the optical flow [3], [4] which provides all information about the movement, but the real-time implementation is difficult and calculation of

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“…Also, it is a challenging task to identify the objects with similar colour and patterns of the static background scenes of a video. Many works have been developed to cope this kind of difficulties, which initialize the background [1][2][3][4], subtracts background scenes [5][6][7], and forefront detection [8][9][10], that supports for video retrieval and detection of moving objects. The background subtraction, background initialization, foreground segmentation, and forefront object detection play a noteworthy role in video analysis like retrieval, summarization, classification, moving object detection and so on.…”

Section: Introductionmentioning

confidence: 99%

“…They have used the histogram intersection method to recognize the objects of interest. The structure and texture components are decomposed, and the background scene is modelled using the median filter; the absolute difference is deployed to subtract the background scenes [32]. They deploy an adaptive threshold that computes the maximum variance difference between the classes.…”

Section: Introductionmentioning

confidence: 99%

Key-Frame Detection and Video Retrieval Based on DC Coefficient-Based Cosine Orthogonality and Multivariate Statistical Tests

Kalakoti¹,

Prabakaran²

2020

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This paper presents a method, which is developed based on the Discrete Cosine (DC) coefficient and multivariate parametric statistical tests, such as tests for equality of mean vectors and the covariance matrices. Background scenes and forefront objects are separated from the key-frame, and the salient features, such as colour and Gabor texture, are extracted from the background and forefront components. The extracted features are formulated as a feature vector. The feature vector is compared to that of the feature vector database, based on the statistical tests. First, the feature vectors are compared with respect to covariance. If the feature vector of the key-frame and the feature vector of the feature vector database pass the test, then the test for equality of mean vector is performed; otherwise, the testing process is stopped. If the feature vectors pass both tests, then it is inferred that the query key-frame represents the target video in the video database. Otherwise, it is concluded that the query key-frame not representing the video; and the proposed system takes the next feature vector for matching. The proposed method results in an average retrieval rate of 97.232%, 96.540%, and 96.641% for CC_WEB, UCF101, and our newly constructed database, respectively. Further, the mAP scores computed for each video datasets, which resulted in 0.807, 0.812, and 0.814 for CC_WEB, UCF101, and our newly constructed database, respectively. The output results obtained by the proposed method are comparable to the existing methods.

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Motion detection based on the combining of the background subtraction and the structure–texture decomposition

Cited by 26 publications

References 7 publications

Real-Time, Content-Based Communication Load Reduction in the Internet of Multimedia Things

Real-Time, Content-Based Communication Load Reduction in the Internet of Multimedia Things

A block-based background model for moving object detection

Key-Frame Detection and Video Retrieval Based on DC Coefficient-Based Cosine Orthogonality and Multivariate Statistical Tests

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