An adaptive background subtraction scheme for video surveillance systems

Shah, Nisarg; Pingale, Akshay; Patel, Vinal; George, Nithin V.

doi:10.1109/isspit.2017.8388311

Cited by 8 publications

(2 citation statements)

References 16 publications

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“…Eigen-Background Subtraction. This technique is used by many authors, such as [27][28][29]. Here, the background is represented by a reconstructed image from a set of dominant eigenvectors.…”

Section: Introductionmentioning

confidence: 99%

Background Subtraction for Dynamic Scenes Using Gabor Filter Bank and Statistical Moments

Romero-González,

Córdova-Esparza,

Terven

et al. 2024

Algorithms

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This paper introduces a novel background subtraction method that utilizes texture-level analysis based on the Gabor filter bank and statistical moments. The method addresses the challenge of accurately detecting moving objects that exhibit similar color intensity variability or texture to the surrounding environment, which conventional methods struggle to handle effectively. The proposed method accurately distinguishes between foreground and background objects by capturing different frequency components using the Gabor filter bank and quantifying the texture level through statistical moments. Extensive experimental evaluations use datasets featuring varying lighting conditions, uniform and non-uniform textures, shadows, and dynamic backgrounds. The performance of the proposed method is compared against other existing methods using metrics such as sensitivity, specificity, and false positive rate. The experimental results demonstrate that the proposed method outperforms other methods in accuracy and robustness. It effectively handles scenarios with complex backgrounds, lighting changes, and objects that exhibit similar texture or color intensity as the background. Our method retains object structure while minimizing false detections and noise. This paper provides valuable insights into computer vision and object detection, offering a promising solution for accurate foreground detection in various applications such as video surveillance and motion tracking.

show abstract

Section: Introductionmentioning

confidence: 99%

Background Subtraction for Dynamic Scenes Using Gabor Filter Bank and Statistical Moments

Romero-González,

Córdova-Esparza,

Terven

et al. 2024

Algorithms

View full text Add to dashboard Cite

show abstract

“…In addition, various morphological operations were used to obtain the outline and bounding box of a moving object, detect moving vehicles, and count the vehicles passing through a designated area. A few researchers have used Gaussian mixture models (GMMs) [9,10] to model the background or adaptive background [11][12][13] with the aim of solving the problem of background subtraction due to background images. Poor foreground segmentation is caused by gradual changes in brightness.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Traffic-Monitoring System Based on YOLO and Convolutional Fuzzy Neural Networks

Lin

Jhang

2022

IEEE Access

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With the rapid pace of urbanization, the number of vehicles traveling between cities has increased significantly. Consequently, many traffic-related problems have emerged, such as traffic jams and excessive numbers and types of vehicles. To solve traffic problems, road data collection is important. Therefore, in this paper, we develop an intelligent traffic-monitoring system based on you only look once (YOLO) and a convolutional fuzzy neural network (CFNN), which record traffic volume, and vehicle type information from the road. In this system, YOLO is first used to detect vehicles and is combined with a vehicle-counting method to calculate traffic flow. Then, two effective models (CFNN and Vector-CFNN) and a network mapping fusion method are proposed for vehicle classification. In our experiments, the proposed method achieved an accuracy of 90.45% on the Beijing Institute of Technology public dataset. On the GRAM-RTM data set, the mean average precision and F-measure (F1) of the proposed YOLO-CFNN and YOLO-VCFNN vehicle classification methods are 99%, superior to those of other methods. On actual roads in Taiwan, the proposed YOLO-CFNN and YOLO-VCFNN methods not only have a high F1 score for vehicle classification but also have outstanding accuracy in vehicle counting. In addition, the proposed system can maintain a detection speed of more than 30 frames per second in the AGX embedded platform. Therefore, the proposed intelligent traffic monitoring system is suitable for real-time vehicle classification and counting in the actual environment.

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Background Subtraction Algorithm Comparison on the Raspberry Pi Platform for Real Video Datasets

Iszaidy

Ngadiran

Ramli

et al. 2022

Lecture Notes in Electrical Engineering

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An adaptive background subtraction scheme for video surveillance systems

Cited by 8 publications

References 16 publications

Background Subtraction for Dynamic Scenes Using Gabor Filter Bank and Statistical Moments

Background Subtraction for Dynamic Scenes Using Gabor Filter Bank and Statistical Moments

Intelligent Traffic-Monitoring System Based on YOLO and Convolutional Fuzzy Neural Networks

Background Subtraction Algorithm Comparison on the Raspberry Pi Platform for Real Video Datasets

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