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

Zuo, Junhui; Jia, Zhenhong; Yang, Jie; Kasabov, Nikola

doi:10.1109/access.2019.2946230

Cited by 33 publications

(22 citation statements)

References 34 publications

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“…Apart from modeling the background, it is easy to use, as the local movements in the backgrounds are prone to noise or complex scenes. Meanwhile, the Gaussian composite (GMM) model [17] is very powerful, but requires a lot of modeling and rear modeling frames respectively, which has high computer cohesion and difficulty handling video frames with varying brightness, something that moves frequently and hides.…”

Section: Related Workmentioning

confidence: 99%

Humanrecognition Using ‘Pso-Ofa’ In Low Resolution Videos

Narayana¹

2021

TURCOMAT

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In present scenario, tracking of target in videos with low resolution is most important task. The problem aroused due to lack of discriminatory data that have low visual visibility of the moving objects. However, earlier detection methods often extract explanations around fascinating points of space or exclude mathematical features in moving regions, resulting in limited capabilities to detect better video functions. To overcome the above problem, in this paper a novel method which recognizes a person from low resolution videos is proposed. A Three step process is implemented in which during the first step, the video data acquired from a low-resolution video i.e. from three different datasets. The acquired video is divided into frames and converted into gray scale from RGB. Secondly, background subtraction is performed using LBP and thereafter Histogram of Optical Flow (HOF) descriptors is extracted from optical flow images for motion estimation. In the third step, the eigen features are extracted and optimized using particle swarm optimization (PSO) model to eliminate redundant information and obtain optimized features from the video which is being processed. Finally to find a person from low resolution videos, the features are classified by Support Vector Machine (SVM) and parameters are evaluated. Experimental results are performed on VIRAT, Soccer and KTH datasets and demonstrated that the proposed detection approach is superior to the previous method

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Section: Related Workmentioning

confidence: 99%

Humanrecognition Using ‘Pso-Ofa’ In Low Resolution Videos

Narayana¹

2021

TURCOMAT

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“…Without modelling background, it is simple to implement, yet is vulnerable to noise or complex scenes with local motion in the background. Meanwhile, Gaussian mixture model (GMM) [13]- [15] is more robust, but it needs multiple frames for modelling and updates the background iteratively, which suffers from high computational complexity and is hard to handle the video frames with illumination variation, infrequently moving object and camouflage.…”

Section: Related Workmentioning

confidence: 99%

Moving Object Detection With Deep CNNs

et al. 2020

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In large field of view for open country, the real-time detection and identification of moving objects with high accuracy is a very challenging work due to the excessive amount of data. This paper proposes a novel framework that consists of a coarse-grained detection as well as a fine-grained detection. To solve the problem of noise-induced object fracture during the coarse-grained detection process, we present a low-complexity connected region detection algorithm to extract moving regions. Furthermore, in the finegrained detection, Deep Convolution Neural Networks are leveraged to detect more precise coordinates and identify the category of objects. To the best of our knowledge, this is the first work that proposes a coarse-tofine grained framework to detect moving objects on high-resolution scenes. Experimental results show that the proposed framework can robustly work on the high resolution video frames (1920*1080p) with complex situations more fastly and accurately over existing methods. INDEX TERMS Connected region detection, deep convolution neural networks, foreground extraction, high resolution, moving object detection.

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“…In terms of the traditional methods, the foreground extraction methods that are designed for stationary cameras commonly consist of three main parts: background model design and initialization, the comparison between the current frame and background model, and background model maintenance. The competitive methods include Gaussian mixture model (GMM) based [ 16 ], sample-based [ 5 ], and codebook based [ 17 ] methods. They are designed to tackle different traditional challenges such as hard shadow, illumination change, dynamic background, and camera jitter, but their applications are limited by the assumption that the camera is stationary.…”

Section: Related Workmentioning

confidence: 99%

Deep Features Homography Transformation Fusion Network—A Universal Foreground Segmentation Algorithm for PTZ Cameras and a Comparative Study

Tao

Zhou

2020

Sensors

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The foreground segmentation method is a crucial first step for many video analysis methods such as action recognition and object tracking. In the past five years, convolutional neural network based foreground segmentation methods have made a great breakthrough. However, most of them pay more attention to stationary cameras and have constrained performance on the pan–tilt–zoom (PTZ) cameras. In this paper, an end-to-end deep features homography transformation and fusion network based foreground segmentation method (HTFnetSeg) is proposed for surveillance videos recorded by PTZ cameras. In the kernel of HTFnetSeg, there is the combination of an unsupervised semantic attention homography estimation network (SAHnet) for frames alignment and a spatial transformed deep features fusion network (STDFFnet) for segmentation. The semantic attention mask in SAHnet reinforces the network to focus on background alignment by reducing the noise that comes from the foreground. STDFFnet is designed to reuse the deep features extracted during the semantic attention mask generation step by aligning the features rather than only the frames, with a spatial transformation technique in order to reduce the algorithm complexity. Additionally, a conservative strategy is proposed for the motion map based post-processing step to further reduce the false positives that are brought by semantic noise. The experiments on both CDnet2014 and Lasiesta show that our method outperforms many state-of-the-art methods, quantitively and qualitatively.

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

Cited by 33 publications

References 34 publications

Humanrecognition Using ‘Pso-Ofa’ In Low Resolution Videos

Humanrecognition Using ‘Pso-Ofa’ In Low Resolution Videos

Moving Object Detection With Deep CNNs

Deep Features Homography Transformation Fusion Network—A Universal Foreground Segmentation Algorithm for PTZ Cameras and a Comparative Study

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