Adaptive GMM and BP Neural Network Hybrid Method for Moving Objects Detection in Complex Scenes

Ou, Xianfeng; Yan, Pengcheng; He, Wei; Kim, Yong Kwan; Zhang, Guoyun; Peng, Xin; Hu, Wenjing; Wu, Jian; Guo, Lei

doi:10.1142/s0218001419500046

Cited by 9 publications

(13 citation statements)

References 11 publications

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“…In background subtraction, if the difference between the mean of the current frame and the Gaussian model is greater than the standard deviation of the Gaussian model by a factor of δ, then the pixel is considered a target pixel; otherwise, the pixel is considered a background pixel. it's the specific expression is shown in formula (16). In the moving target detection phase, especially under a dynamic background, the main problem is noise.…”

Section: B Moving Target Detectionmentioning

confidence: 99%

“…For moving target detection in different scenarios, the detection effect varied due to background changes in the detection [24]; (e) PBAS [14]; (f) FM [15]; (g) IPVB [25]; (h) AGBP [16]; (i) proposed method. [24]; (e) PBAS [14]; (f) FM [15]; (g) IPVB [25]; (h) AGBP [16]; (i) proposed method.…”

Section: B Moving Target Detectionmentioning

confidence: 99%

“…This method can quickly detect moving targets; however, these moving targets are incomplete, and voids occur, which reduces the detection performance of this algorithm. In [16], a hybrid method of adaptive GMM and BP neural network (AGBP) is proposed to extract foreground targets from complex scenes. Compared with the original GMM algorithm, the detection effect of this method is greatly improved.…”

Section: Introductionmentioning

confidence: 99%

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Moving Target Detection Based on Improved Gaussian Mixture Background Subtraction in Video Images

et al. 2019

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In recent years, background subtraction techniques have been used in vision and image applications for moving target detection. However, most methods cannot provide fine results due to dynamic backgrounds, noise, etc. The Gaussian mixture model (GMM) is a background modeling method commonly used in moving target detection. The traditional GMM method is vulnerable to noise interference, especially from dynamic backgrounds; thus, its detection performance is not good. Because of the influence of background noise and dynamic effects on moving target detection, we propose a method of moving target detection for dynamic backgrounds based on improved GMM background subtraction. This method can be divided into three stages. First, in the background modeling stage, to facilitate calculation and improve modeling speed, the video frame is blocked, and the background model is reconstructed using the image block averaging method. Second, in the moving target detection stage, the method of combining wavelet semi-threshold function denoising with mathematical morphology closed operation is used for denoising, which effectively eliminates the influence of noise and improves the detection effect. Third, in the background updating stage, the adaptive background updating method is used to update the background to improve detection results. The simulation results show that the improved method can reduce noise and dynamic background interference while improving moving target detection, thereby proving the effectiveness and adaptability of the proposed method.INDEX TERMS Gaussian mixture model, moving target detection, dynamic background, mathematical morphology, adaptive background updating.

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Section: B Moving Target Detectionmentioning

confidence: 99%

Section: B Moving Target Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Moving Target Detection Based on Improved Gaussian Mixture Background Subtraction in Video Images

et al. 2019

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“…Object detection is a fundamental problem in computer vision [9,10,11,12]. Driven by various Convolutional Neural Networks (CNNs), there are two different approaches to perform object detection: two-stage detectors and one-stage detectors [13,14,15].…”

Section: Related Workmentioning

confidence: 99%

Low-Cost Image Compressive Sensing with Multiple Measurement Rates for Object Detection

Liao

Yang

et al. 2019

Sensors

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When measurement rates grow, most Compressive Sensing (CS) methods suffer from an increase in overheads of transmission and storage of CS measurements, while reconstruction quality degrades appreciably when measurement rates reduce. To solve these problems in real scenarios such as large-scale distributed surveillance systems, we propose a low-cost image CS approach called MRCS for object detection. It predicts key objects using the proposed MYOLO3 detector, and then samples the regions of the key objects as well as other regions using multiple measurement rates to reduce the size of sampled CS measurements. It also stores and transmits half-precision CS measurements to further reduce the required transmission bandwidth and storage space. Comprehensive evaluations demonstrate that MYOLO3 is a smaller and improved object detector for resource-limited hardware devices such as surveillance cameras and aerial drones. They also suggest that MRCS significantly reduces the required transmission bandwidth and storage space by declining the size of CS measurements, e.g., mean Compression Ratios (mCR) achieves 1.43–22.92 on the VOC-pbc dataset. Notably, MRCS further reduces the size of CS measurements by half-precision representations. Subsequently, the required transmission bandwidth and storage space are reduced by one half as compared to the counterparts represented with single-precision floats. Moreover, it also substantially enhances the usability of object detection on reconstructed images with half-precision CS measurements and multiple measurement rates as compared to its counterpart, using a single low measurement rate.

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“…Sengar and Mukhopadhyay [24] introduce the target boundary extraction mechanism based on the optical flow model to extract the moving object more completely. Ou et al [25] build a GMM model and introduces learning factors to dynamically update the foreground and background. Chavan and Gengaje [26] combine a GMM model with an optical flow method to extract the target hierarchically.…”

Section: Introductionmentioning

confidence: 99%

A Data Encryption and Fast Transmission Algorithm Based on Surveillance Video

Qiu

Cui²,

Meng

2020

Wireless Communications and Mobile Computing

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Video surveillance is an effective way to record current events. In view of the difficulty of efficient transmission of massive surveillance video and the risk of leakage in the transmission process, a new data encryption and fast transmission algorithm is proposed in this paper. From the perspective of events, the constraints of time and space dimension is broken. First, a background and moving object extraction model is built based on video composition. Then, a strong correlation data encryption and fast transmission model is constructed to achieve efficient data compression. Finally, a data mapping mechanism is established to realize the decoding of surveillance video. Our experimental results show that the compression ratio of the proposed algorithm is more than 60% under the premise of image confidentiality.

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Adaptive GMM and BP Neural Network Hybrid Method for Moving Objects Detection in Complex Scenes

Cited by 9 publications

References 11 publications

Moving Target Detection Based on Improved Gaussian Mixture Background Subtraction in Video Images

Moving Target Detection Based on Improved Gaussian Mixture Background Subtraction in Video Images

Low-Cost Image Compressive Sensing with Multiple Measurement Rates for Object Detection

A Data Encryption and Fast Transmission Algorithm Based on Surveillance Video

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