Motion detection based on the combining of the background subtraction and spatial color information

Harrouss, Omar El; Moujahid, Driss; Tairi, Hamid

doi:10.1109/isacv.2015.7105548

Cited by 19 publications

(4 citation statements)

References 2 publications

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“…In video surveillance, scene control is one of the main difficulties. Historically, this problem has been addressed using active sensors, such as sonar, laser, and radar sensors [19][20][21][22]. Thanks to technological progress, nowadays, video surveillance mostly relies on IP cameras, which enable direct monitoring of the relevant scenes.…”

Section: Related Workmentioning

confidence: 99%

Deep-Learning-Based Action and Trajectory Analysis for Museum Security Videos

Di Maio,

Nunziati,

Mecocci

2024

Electronics

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Recent advancements in deep learning and video analysis, combined with the efficiency of contemporary computational resources, have catalyzed the development of advanced real-time computational systems, significantly impacting various fields. This paper introduces a cutting-edge video analysis framework that was specifically designed to bolster security in museum environments. We elaborate on the proposed framework, which was evaluated and integrated into a real-time video analysis pipeline. Our research primarily focused on two innovative approaches: action recognition for identifying potential threats at the individual level and trajectory extraction for monitoring museum visitor movements, serving the dual purposes of security and visitor flow analysis. These approaches leverage a synergistic blend of deep learning models, particularly CNNs, and traditional computer vision techniques. Our experimental findings affirmed the high efficacy of our action recognition model in accurately distinguishing between normal and suspicious behaviors within video feeds. Moreover, our trajectory extraction method demonstrated commendable precision in tracking and analyzing visitor movements. The integration of deep learning techniques not only enhances the capability for automatic detection of malevolent actions but also establishes the trajectory extraction process as a robust and adaptable tool for various analytical endeavors beyond mere security applications.

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Section: Related Workmentioning

confidence: 99%

Deep-Learning-Based Action and Trajectory Analysis for Museum Security Videos

Di Maio,

Nunziati,

Mecocci

2024

Electronics

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“…In order to extract the moving objects area called foreground, a thresholding operation is applied. The two principal difficulties of all these methods are the variation in the scene and environmental changes [1–34].…”

Section: Introductionmentioning

confidence: 99%

Moving object detection zone using a block‐based background model

Elharrouss

Abbad

Moujahid

et al. 2017

IET Computer Vision

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Background modelling is a critical case for background-subtraction-based approaches and also for a wide range of applications. The background generation becomes difficult when the scene is complex or an object stays for a long time in the scene. Here, the authors propose a block-based background initialisation, using the sum of absolute difference (SAD), and modelling, using a block-based entropy evaluation, with a low computational cost which making them feasible for embedded platform. In general, many background-subtraction approaches are sensitive to sudden illumination change in the scene and cannot update the background image in scenes. The proposed background modelling approach analyses the illumination change problem. The moving object detection mask is developed using a threshold selected by computing the mean of the SAD between the blocks background and the blocks of the current frame. From the qualitative and quantitative results obtained by the authors approach compared with some existing methods, the authors approach is effective for background generation and moving objects detection.

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“…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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Motion detection based on the combining of the background subtraction and spatial color information

Cited by 19 publications

References 2 publications

Deep-Learning-Based Action and Trajectory Analysis for Museum Security Videos

Deep-Learning-Based Action and Trajectory Analysis for Museum Security Videos

Moving object detection zone using a block‐based background model

A block-based background model for moving object detection

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