Compute-Extensive Background Subtraction for Efficient Ghost Suppression

Singh, Rimjhim Padam; Sharma, Poonam; Madarkar, Jitendra

doi:10.1109/access.2019.2937402

Cited by 13 publications

(4 citation statements)

References 51 publications

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“…It suppresses the effect of ghosting on the background model. Singh RP et al [15] proposed a pixel sample consensus technique for segmenting the foreground, which uses a segmentation mask approach to analyze the possibility of being absorbed and speed up ghost image suppression. However, it is too computationally intensive and cannot effectively eliminate the ghost region.…”

Section: Related Workmentioning

confidence: 99%

An Improved Vibe Algorithm Based on Adaptive Thresholding and the Deep Learning-Driven Frame Difference Method

Liu,

Wei,

Yang

et al. 2023

Electronics

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Foreground detection is the main way to identify regions of interest. The detection effectiveness determines the accuracy of subsequent behavior analysis. In order to enhance the detection effect and optimize the problems of low accuracy, this paper proposes an improved Vibe algorithm combining the frame difference method and adaptive thresholding. First, we adopt a shallow convolutional layer of VGG16 to extract the lower-level features of the image. Features images with high correlation are fused into a new image. Second, adaptive factors based on the spatio-temporal domain are introduced to divide the foreground and background. Finally, we construct an inter-frame average speed value to measure the moving speed of the foreground, which solves the mismatch problem between background change rate and model update rate. Experimental results show that our algorithm can effectively solve the drawback of the traditional method and prevent the background model from being contaminated. It suppresses the generation of ghosting, significantly improves detection accuracy, and reduces the false detection rate.

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Section: Related Workmentioning

confidence: 99%

An Improved Vibe Algorithm Based on Adaptive Thresholding and the Deep Learning-Driven Frame Difference Method

Liu,

Wei,

Yang

et al. 2023

Electronics

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“…In works [60], [61] and [62], the authors consider this task from the point of background subtraction. The authors of [63] focus on various types of methods connected to human activity recognition.…”

Section: B Automotive Perception Systemsmentioning

confidence: 99%

Efficient Characterization Method for Big Automotive Datasets Used for Perception System Development and Verification

Kowalczyk¹,

Komorkiewicz²,

Skruch

et al. 2022

IEEE Access

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T HE paper proposes a formal approach for describing and evaluating the datasets that are used in automotive applications for machine learning, testing, and validation purposes. Proper, that is, qualitative and quantitative characterization of the datasets can simplify the analysis, evaluation, and comparison of perception-based algorithms designed for highly automated vehicles. Such formalism is also needed to achieve compliance with the automotive industry safety standards that have been recently introduced. Characterization in the form of size or type of raw data, number of recognized and classified objects, and environmental parameters is not perfectly suitable for describing both the static and dynamic aspects of automotive datasets; therefore, another approach is required. In this paper, an efficient method based on an object tracking mechanism, grid representation of the sensor field of view, heatmap concept, and Wasserstein metric is proposed. The efficiency of the method is demonstrated by its ability to handle both the size, properties, and diversity of the dataset, including static and time-varying aspects. The presented description can also be used to compare different datasets and to define the amount of data to be collected.

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“…In MOG2, k (number of Gaussian distributions) are selected dynamically for every pixel rather than keeping k constant throughout. The model is selected for background detection because it produces robust and efficient results for lower illumination variations compared to other methods [46].…”

Section: A Feature Of Interest (Foi) Detectionmentioning

confidence: 99%

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

et al. 2020

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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.

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Compute-Extensive Background Subtraction for Efficient Ghost Suppression

Cited by 13 publications

References 51 publications

An Improved Vibe Algorithm Based on Adaptive Thresholding and the Deep Learning-Driven Frame Difference Method

An Improved Vibe Algorithm Based on Adaptive Thresholding and the Deep Learning-Driven Frame Difference Method

Efficient Characterization Method for Big Automotive Datasets Used for Perception System Development and Verification

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

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