An improved Gaussian Mixture Method based Background Subtraction Model for Moving Object Detection in Outdoor Scene

Agrawal, Supriya; Natu, Prachi

doi:10.1109/icecct52121.2021.9616883

“…Agrawal and Natu [37] developed GMM with BLOB analysis of the interconnected parts, including labelling and morphological operations, to increase the accuracy of foreground detection. The suggested model can be broken down into two stages: the training phase, which is responsible for producing a referential image, and the testing phase, which is in charge of producing a binary mask.…”

Section: Sudden Illumination Changementioning

confidence: 99%

Background subtraction challenges in motion detection using Gaussian mixture model: a survey

Aris

¹

,

Jamaian

²

2023

IJ-AI

0

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<p>Motion detection is becoming prominent for computer vision applications. The background subtraction method that uses the Gaussian mixture model (GMM) is utilized frequently in camera or video settings. However, there is still more work that needs to be done to develop a reliable, accurate and high-performing technique due to various challenges. The degree of difficulty for this challenge is primarily determined by how the object to be detected is defined. It could be influenced by the changes in the object posture or deformations. In this context, we describe and bring together the most significant challenges faced by the background subtraction techniques based on GMM for dealing with a crucial background situation. Therefore, the findings of this study can be used to identify the most appropriate GMM version based on the crucial background situation.</p>

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“…[18] proposed a novel moving object detection algorithm with dynamic mode decomposition and YOLOv5, the moving object buried in the dynamic foreground and reconstructed images or videos were recognized by Yolov5. Agrawal et al [19] a novel approach to detect moving objects from static scenes using a single stationary camera, which mainly utilizes the statistical background model Gaussian Mixture Model (GMM) to generate the binary mask, and at this stage, the model parameters were adjusted aiming to update background model pixel-wise. Additionally, Wang et al [20] through another logical thought meaning that random selection of static frames and adding into every other frame to be free from algorithmic dependence on the background and decrease the influence caused by changes in the background.…”

Section: Introductionmentioning

confidence: 99%

Moving Object Detection and Deepsort Fusion for Dynamic Object Tracking

dou,

Tu

2024

Preprint

0

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For the problem of space dynamic target tracking with occlusion, this paper proposes an online tracking method based on the combination between the five-frame difference and Deepsort (Simple Online and Realtime Tracking with a Deep Association Metric), which is to achieve the identification first and then tracking of the dynamic target. First of all, according to three-frame difference, the five-frame difference is improved, and through the integration with ViBe (Visual Background Extraction), the accuracy and anti-interference ability are enhanced; Secondly, the YOLOv5s (You Look Only Once) is improved using preprocessing of DWT (Discrete Wavelet Transformation) and injecting GAM (Global Attention Module), which is considered as the detector for Deepsort to solve the missing in occlusion, and the real-time and accuracy can be strengthened; Lastly, simulation results show that the proposed space dynamic target tracking can keep stable to track all dynamic targets under the background interference and occlusion, the tracking precision is improved to 93.88%. Furthermore, there is a combination with the physical depth camera D435i, experiments on target dynamics show the effectiveness and superiority of the proposed recognition and tracking algorithm in the face of strong light and occlusion.

show abstract

“…Reference 18 proposed a novel moving object detection algorithm with dynamic mode decomposition and YOLOv5, the moving object buried in the dynamic foreground and reconstructed images or videos were recognized by Yolov5. Agrawal et al 19 a novel approach to detect moving objects from static scenes using a single stationary camera, which mainly utilizes the statistical background model Gaussian Mixture Model (GMM) to generate the binary mask, and at this stage, the model parameters were adjusted aiming to update background model pixel-wise. Additionally, Wang et al 20 through another logical thought meaning that random selection of static frames and adding into every other frame to be free from algorithmic dependence on the background and decrease the influence caused by changes in the background.…”

mentioning

confidence: 99%

Space dynamic target tracking method based on five-frame difference and Deepsort

Huang,

Zeng,

Xiong

et al. 2024

Sci Rep

1

0

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For the problem of space dynamic target tracking with occlusion, this paper proposes an online tracking method based on the combination between the five-frame difference and Deepsort (Simple Online and Realtime Tracking with a Deep Association Metric), which is to achieve the identification first and then tracking of the dynamic target. First of all, according to three-frame difference, the five-frame difference is improved, and through the integration with ViBe (Visual Background Extraction), the accuracy and anti-interference ability are enhanced; Secondly, the YOLOv5s (You Look Only Once) is improved using preprocessing of DWT (Discrete Wavelet Transformation) and injecting GAM (Global Attention Module), which is considered as the detector for Deepsort to solve the missing in occlusion, and the real-time and accuracy can be strengthened; Lastly, simulation results show that the proposed space dynamic target tracking can keep stable to track all dynamic targets under the background interference and occlusion, the tracking precision is improved to 93.88%. Furthermore, there is a combination with the physical depth camera D435i, experiments on target dynamics show the effectiveness and superiority of the proposed recognition and tracking algorithm in the face of strong light and occlusion.

show abstract

An improved Gaussian Mixture Method based Background Subtraction Model for Moving Object Detection in Outdoor Scene

Cited by 9 publications

References 18 publications

Background subtraction challenges in motion detection using Gaussian mixture model: a survey

Background subtraction challenges in motion detection using Gaussian mixture model: a survey

Moving Object Detection and Deepsort Fusion for Dynamic Object Tracking

Space dynamic target tracking method based on five-frame difference and Deepsort

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