Fast background subtraction with adaptive block learning using expectation value suitable for real-time moving object detection

Montero, Vince Jebryl; Jung, Wooyoung; Jeong, Youngjin

doi:10.1007/s11554-020-01058-8

Cited by 14 publications

(10 citation statements)

References 28 publications

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“…As seen in Fig. 7, SDMBS provides equivalent results to FBS-ABL [42], although it is more accurate, as seen in Fig. 6.…”

Section: Quantitative Analysismentioning

confidence: 79%

“…When we compared our results to different existing methods published on the CDnet website [39], we identified MOG [9], KNN [40], ViBe [12], and SubS [41] as candidates. Thus, we compared our proposed compressed-based background subtraction SDMBS with recent and state-of-theart methods [26,42], classical methods like [9,40], and fast methods like the ViBe [12] Background Subtraction Algorithm.…”

Section: B Qualitative Analysismentioning

confidence: 99%

“…In [26], a block-based MOG is designed to be processed using the compressed sensing CS elements of the frame-blocks CS-MOG and is targeted at WVSN applications, whereas [42] is a background model update algorithm that uses an intermittent technique along with an adaptive block-learning algorithm.…”

Section: B Qualitative Analysismentioning

confidence: 99%

“…The original video frame for the three datasets and its corresponding groundtruth are shown in the top two rows. The results of MOG [9], Vibe [12], two current state-of-the-art techniques [26,42], and SDMBS are shown in the next five rows (from top to bottom). In the last row of Fig.…”

Section: B Qualitative Analysismentioning

confidence: 99%

See 3 more Smart Citations

Moving Object Detection over Wireless Visual Sensor Networks using Spectral Dual Mode Background Subtraction

AbdelTawab¹,

Abdelhalim²,

Habib³

2022

IJACSA

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Wireless Visual Sensor Networks (WVSN) play an essential role in tracking moving objects. WVSN's key drawbacks are storage, power, and bandwidth. Background subtraction is used in the early stages of target tracking to extract moving targets from video images. Many standard methods of subtracting backgrounds are no longer suitable for embedded devices because they use complex statistical models to manage small changes in lighting. This paper introduces a system based on the Partial Discrete Cosine Transform (PDCT), reducing the vast dimensions of processed data while retaining most of the important information, thereby reducing processing and transmission energy. It also uses a dual-mode single Gaussian model (SGM) for accurate detection of moving objects. The proposed system's performance is to be assessed using the standard CDnet 2014 benchmark dataset in terms of detection accuracy and time complexity. Furthermore, the suggested method is compared to previous WVSN background subtraction methods. Simulation results show that the proposed method consistently has 15% better accuracy and is up to 3 times faster than the state-of-the-art object detection methods for WVSN. Finally, we showed the practicality of the suggested method by simulating it in a sensor network environment using the Contiki OS Cooja Simulator and implementing it in a real testbed using Cortex M3 open nodes of IOT-LAB.

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“…As seen in Fig. 7, SDMBS provides equivalent results to FBS-ABL [42], although it is more accurate, as seen in Fig. 6.…”

Section: Quantitative Analysismentioning

confidence: 79%

Section: B Qualitative Analysismentioning

confidence: 99%

Section: B Qualitative Analysismentioning

confidence: 99%

Section: B Qualitative Analysismentioning

confidence: 99%

See 2 more Smart Citations

Moving Object Detection over Wireless Visual Sensor Networks using Spectral Dual Mode Background Subtraction

AbdelTawab¹,

Abdelhalim²,

Habib³

2022

IJACSA

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show abstract

“…Other proposals for achieving the segmentation of an image are based on the concept of saliency [23], given that a salient object is clearly differentiated from the background. Commonly, background subtraction is done by the detection of moving objects against a static background [24][25][26][27]. These techniques are effective in certain contexts, but this approach has problems when the scenes are dynamic or when the camera is not static; these situations have been treated with some compensations of the camera movements and with the updating of the background of the model [28,29].…”

mentioning

confidence: 99%

Plant Disease Diagnosis in the Visible Spectrum

2022

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A simple and robust methodology for plant disease diagnosis using images in the visible spectrum of plants, even in uncontrolled environments, is presented for possible use in mobile applications. This strategy is divided into two main parts: on the one hand, the segmentation of the plant, and on the other hand, the identification of color associated with diseases. Gaussian mixture models and probabilistic saliency segmentation are used to accurately segment the plant from the background of an image, and HSV thresholds are used in order to achieve the identification and quantification of the colors associated with the diseases. Proper identification of the colors associated with diseases of interest combined with adequate segmentation of the plant and the background produces a robust diagnosis in a wide range of scenarios.

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ABGS Segmenter: pixel wise adaptive background subtraction and intensity ratio based shadow removal approach for moving object detection

Agrawal

Natu²

2022

J Supercomput

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Fast background subtraction with adaptive block learning using expectation value suitable for real-time moving object detection

Cited by 14 publications

References 28 publications

Moving Object Detection over Wireless Visual Sensor Networks using Spectral Dual Mode Background Subtraction

Moving Object Detection over Wireless Visual Sensor Networks using Spectral Dual Mode Background Subtraction

Plant Disease Diagnosis in the Visible Spectrum

ABGS Segmenter: pixel wise adaptive background subtraction and intensity ratio based shadow removal approach for moving object detection

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