Review of background subtraction methods using Gaussian mixture model for video surveillance systems

Goyal, Kalpana; Singhai, Jyoti

doi:10.1007/s10462-017-9542-x

Cited by 84 publications

(41 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Over the years, researchers have proposed various background modeling methods to distinguish the foreground and background in a video. The most extensively used pixel-based parametric background modeling methods are the Gaussian Mixture Model (GMM), and Adaptive GMM (AGMM) [43]. In Mixture of Gaussians (MOG) modeling, each pixel is modeled by more than one (k) Gaussians per pixel (multiple Gaussian distributions) to observe the variations in the color of a pixel in Red-Green-Blue (RGB) color space at any time t. A pixel frequently observed in the recent past that does not fit the k distributions is labeled as foreground.…”

Section: A Feature Of Interest (Foi) Detectionmentioning

confidence: 99%

See 1 more Smart Citation

MuLViS: Multi-Level Encryption Based Security System for Surveillance Videos

et al. 2020

View full text Add to dashboard Cite

Video Surveillance (VS) systems are commonly deployed for real-time abnormal event detection and autonomous video analytics. Video captured by surveillance cameras in real-time often contains identifiable personal information, which must be privacy protected, sometimes along with the locations of the surveillance and other sensitive information. Within the Surveillance System, these videos are processed and stored on a variety of devices. The processing and storage heterogeneity of those devices, together with their network requirements, make real-time surveillance systems complex and challenging. This paper proposes a surveillance system, named as Multi-Level Video Security (MuLViS) for privacyprotected cameras. Firstly, a Smart Surveillance Security Ontology (SSSO) is integrated within the MuLViS, with the aim of autonomously selecting the privacy level matching the operating device's hardware specifications and network capabilities. Overall, along with its device-specific security, the system leads to relatively fast indexing and retrieval of surveillance video. Secondly, information within the videos are protected at the times of capturing, streaming, and storage by means of differing encryption levels. An extensive evaluation of the system, through visual inspection and statistical analysis of experimental video results, such as by the Encryption Space Ratio (ESR), has demonstrated the aptness of the security level assignments. The system is suitable for surveillance footage protection, which can be made General Data Protection Regulation (GDPR) compliant, ensuring that lawful data access respects individuals' privacy rights.

show abstract

Section: A Feature Of Interest (Foi) Detectionmentioning

confidence: 99%

“…The number of MVDs is dependent on the motion within a video. Background detection is performed by the optimal MOG2 [43] method. The methods implemented in the processing module are chosen because of their robustness and efficiency compared to other background subtraction algorithms.…”

Section: Real Time Pre-processing Modulementioning

confidence: 99%

MuLViS: Multi-Level Encryption Based Security System for Surveillance Videos

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Based on variance and persistence, Gaussians categorized as "foreground" and "background". The value of pixels if not represent the background distributions then take into the foreground this will be done until it is Gaussian with consistent and sufficient evidence supported [13].…”

Section: Background Subtractionmentioning

confidence: 99%

Abnormal activity detection in surveillance video scenes

2020

View full text Add to dashboard Cite

Automated detection of abnormal activity assumes a significant task in surveillance applications. This paper presents an intelligent framework video surveillance to detect abnormal human activity in an academic environment that takes into account the security and emergency aspects by focusing on three abnormal activities (falling, boxing and waving). This framework designed to consist of the two essential processes: the first one is a tracking system that can follow targets with identify sets of features to understand human activity and measure descriptive information of each target. The second one is a decision system that can realize if the activity of the target track is "normal" or "abnormal" then energizing alarm when recognized abnormal activities.

show abstract

“…To solve those problems with plug inspection, we design a VIS using a change detection framework. Although the concept of change detection is already used in the fields of remote sensing [19,20], video surveillance [21], and medical diagnosis and treatment [22], the change detection framework proposed in this paper is designed especially for railways. Because the detected objects vary based on the application, it is difficult to compare algorithms using the change detection framework [23].…”

Section: Introductionmentioning

confidence: 99%

Change Detection: The Framework of Visual Inspection System for Railway Plug Defects

Cheng

2020

IEEE Access

View full text Add to dashboard Cite

Railway plug defects impact the safety of a railway system. To detect railway plug defects, we establish the framework of a visual inspection system (VIS), which is the first system that can perform railway plug inspection automatically and intelligently. Using the idea of change detection, the framework includes three algorithm modules, which are named the object location, image alignment and similarity measurement modules. After the image acquisition system captures a rail image as the input, the three algorithm modules process the image in order. First, in the object location module, a deep convolutional neural network is used to perform plug location. Second, in the image alignment module, a simple and fast method is designed to align key images using histogram of oriented gradients features. Third, in the similarity measurement module, the χ2 distance is used to compute the similarity between the two plug regions in an inspection image and in an aligned ground-truth image. The results of the similarity measurement are sorted when all inspection images are processed. Therefore, the inspection images with smaller similarity values are ranked higher and the plugs in the images have larger probabilities of defects. The framework has passed the practice tests, and the visual inspection system using this framework has already been authorized by the China Railway Corporation and will be equipped in many inspection trains belonging to local railway corporations.

show abstract

Review of background subtraction methods using Gaussian mixture model for video surveillance systems

Cited by 84 publications

References 27 publications

MuLViS: Multi-Level Encryption Based Security System for Surveillance Videos

MuLViS: Multi-Level Encryption Based Security System for Surveillance Videos

Abnormal activity detection in surveillance video scenes

Change Detection: The Framework of Visual Inspection System for Railway Plug Defects

Contact Info

Product

Resources

About