Moving object detection using statistical background subtraction in wavelet compressed domain

Sengar, Sandeep Singh; Mukhopadhyay, Susanta

doi:10.1007/s11042-019-08506-z

Cited by 32 publications

(10 citation statements)

References 45 publications

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“…The background subtraction algorithm works on the principle of subtracting a background image from the to-be-processed image to obtain a target image without background interference. It is commonly used in foreground detection for machine vision [22][23][24]. The calculation procedure is shown in equation (1):…”

Section: Simulation Results and Analysismentioning

confidence: 99%

Phased array ultrasonic S-scan testing of near-detection-surface defects based on a background subtraction algorithm

Tian

Wei

et al. 2022

Mater. Res. Express

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In phased array ultrasonic sector scanning testing, echoes from defects near the detection surface are often overlapped with interface echoes and cannot be characterized. Based on linear acoustic theory, a mathematical model combining the background subtraction and the sum of squares differences algorithm is established to extract the characteristics of near-detection-surface defect echoes. The upper surface of artificial notch defect is used as a near-detection-surface defect for detection. Specially, linear interpolation algorithm is used to suppress the effect of residual interface echo on the defect echo feature extraction and the effect of different interpolation factors is analyzed. The defect location and size were calculated based on the extracted defect characteristics. The simulation and experimental results indicate that the model can effectively extract the near-detection-surface defect echoes characteristics. Furthermore, when the interpolated signal reaches at least 500 MHz, residual interface echoes can be effectively suppressed. More importantly, according to the extracted defect echo characteristics, the localization and quantitative accuracies of near-surface defects can reach 0.2 mm and 0.3 mm, respectively. These findings provide an effective strategy for phased array ultrasonic sector scanning testing of near-surface defects.

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Section: Simulation Results and Analysismentioning

confidence: 99%

Phased array ultrasonic S-scan testing of near-detection-surface defects based on a background subtraction algorithm

Tian

Wei

et al. 2022

Mater. Res. Express

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“…It integrates multiple technologies such as artificial intelligence, and image recognition. So, it is widely used in national defense, military, and other fields [ 1 – 5 ]. Traditional object detection is mainly for simple action scenes.…”

Section: Related Workmentioning

confidence: 99%

Detection of Aerobics Action Based on Convolutional Neural Network

Zhang

2022

Computational Intelligence and Neuroscience

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To further improve the accuracy of aerobics action detection, a method of aerobics action detection based on improving multiscale characteristics is proposed. In this method, based on faster R-CNN and aiming at the problems existing in faster R-CNN, the feature pyramid network (FPN) is used to extract aerobics action image features. So, the low-level semantic information in the images can be extracted, and it can be converted into high-resolution deep-level semantic information. Finally, the target detector is constructed by the above-extracted anchor points so as to realize the detection of aerobics action. The results show that the loss function of the neural network is reduced to 0.2 by using the proposed method, and the accuracy of the proposed method can reach 96.5% compared with other methods, which proves the feasibility of this study.

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“…To reduce the storage space and time required, a background statistical subtraction approach [29] based on motion segmentation in the compression transform domain using Wavelet has been proposed. A good observation was made in 8x8 blocks using the DCT coefficients of the precoded JPEG image [22].…”

Section: ) Compressed Data: According To Robust Primarymentioning

confidence: 99%

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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Moving object detection using statistical background subtraction in wavelet compressed domain

Cited by 32 publications

References 45 publications

Phased array ultrasonic S-scan testing of near-detection-surface defects based on a background subtraction algorithm

Phased array ultrasonic S-scan testing of near-detection-surface defects based on a background subtraction algorithm

Detection of Aerobics Action Based on Convolutional Neural Network

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

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