Coarse-to-fine sample-based background subtraction for moving object detection

Xu, Yiping; Ji, Hongbing; Zhang, Wenbo

doi:10.1016/j.ijleo.2020.164195

Cited by 15 publications

(5 citation statements)

References 34 publications

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“…As a result, the survey is divided into three sections in the following.Non-AI-based techniquesOne of the most used strategies for finding items is background removal (Kalsotra and Arora, 2021, Maddalena and Petrosino, 2018, Xu et al ., 2020). This programme compares the moving elements of a movie with images of the background and foreground.The optical flow analysis approach (Diamond et al ., 2017) is a way for describing how things move over a series of pictures with a little time gap between them, like video frames.…”

Section: Related Workmentioning

confidence: 99%

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Employing a multi-sensor fusion array to detect objects for an orbital transfer vehicle to remove space debris

Jani

2023

IJIUS

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PurposeThis article takes into account object identification, enhanced visual feature optimization, cost effectiveness and speed selection in response to terrain conditions. Neither supervised machine learning nor manual engineering are used in this work. Instead, the OTV educates itself without instruction from humans or labeling. Beyond its link to stopping distance and lateral mobility, choosing the right speed is crucial. One of the biggest problems with autonomous operations is accurate perception. Obstacle avoidance is typically the focus of perceptive technology. The vehicle's shock is nonetheless controlled by the terrain's roughness at high speeds. The precision needed to recognize difficult terrain is far higher than the accuracy needed to avoid obstacles.Design/methodology/approachRobots that can drive unattended in an unfamiliar environment should be used for the Orbital Transfer Vehicle (OTV) for the clearance of space debris. In recent years, OTV research has attracted more attention and revealed several insights for robot systems in various applications. Improvements to advanced assistance systems like lane departure warning and intelligent speed adaptation systems are eagerly sought after by the industry, particularly space enterprises. OTV serves as a research basis for advancements in machine learning, computer vision, sensor data fusion, path planning, decision making and intelligent autonomous behavior from a computer science perspective. In the framework of autonomous OTV, this study offers a few perceptual technologies for autonomous driving in this study.FindingsOne of the most important steps in the functioning of autonomous OTVs and aid systems is the recognition of barriers, such as other satellites. Using sensors to perceive its surroundings, an autonomous car decides how to operate on its own. Driver-assistance systems like adaptive cruise control and stop-and-go must be able to distinguish between stationary and moving objects surrounding the OTV.Originality/valueOne of the most important steps in the functioning of autonomous OTVs and aid systems is the recognition of barriers, such as other satellites. Using sensors to perceive its surroundings, an autonomous car decides how to operate on its own. Driver-assistance systems like adaptive cruise control and stop-and-go must be able to distinguish between stationary and moving objects surrounding the OTV.

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Section: Related Workmentioning

confidence: 99%

“…One of the most used strategies for finding items is background removal (Kalsotra and Arora, 2021, Maddalena and Petrosino, 2018, Xu et al ., 2020). This programme compares the moving elements of a movie with images of the background and foreground.…”

Section: Related Workmentioning

confidence: 99%

Employing a multi-sensor fusion array to detect objects for an orbital transfer vehicle to remove space debris

Jani

2023

IJIUS

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“…In the past years, algorithms for the detection of small space objects have been extensively studied and can be categorized as follows: Background subtraction methods [ 83 , 84 , 85 ] are one of the most popular approaches for detecting objects. This algorithm works by comparing the moving parts of a video with a background and a foreground image.…”

Section: Related Workmentioning

confidence: 99%

“…Background subtraction methods [ 83 , 84 , 85 ] are one of the most popular approaches for detecting objects. This algorithm works by comparing the moving parts of a video with a background and a foreground image.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Methods for Space Situational Awareness in Mega-Constellations Satellite-Based Internet of Things Networks

Massimi

Ferrara²,

Benedetto

2022

Sensors

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Artificial Intelligence of things (AIoT) is the combination of Artificial Intelligence (AI) technologies and the Internet of Things (IoT) infrastructure. AI deals with the devices’ learning process to acquire knowledge from data and experience, while IoT concerns devices interacting with each other using the Internet. AIoT has been proven to be a very effective paradigm for several existing applications as well as for new areas, especially in the field of satellite communication systems with mega-constellations. When AIoT meets space communications efficiently, we have interesting uses of AI for Satellite IoT (SIoT). In fact, the number of space debris is continuously increasing as well as the risk of space collisions, and this poses a significant threat to the sustainability and safety of space operations that must be carefully and efficiently addressed to avoid critical damage to the SIoT networks. This paper aims to provide a systematic survey of the state of the art, challenges, and perspectives on the use of deep learning methods for space situational awareness (SSA) object detection and classification. The contributions of this paper can be summarized as follows: (i) we outline using AI algorithms, and in particular, deep learning (DL) methods, the possibility of identifying the nature/type of spatial objects by processing signals from radars; (ii) we present a comprehensive taxonomy of DL-based methods applied to SSA object detection and classification, as well as their characteristics, and implementation issues.

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“…After the fitting process, the model is able to decide if the incoming pixels belong to the BG, based on the probability of the MoG identified at each pixel location. The BG model in their work has inspired the development of many variants of BG models, such as the texture-based approach [2], improved GMM [3] [4], and other novel approaches [5] [6] [7] [8]. Despite its popularity, the GMM-based model like the Stauffer-Grimson model still has a problem in dealing with a noisy background.…”

Section: Introductionmentioning

confidence: 99%

Moving Pedestrian Localization and Detection With Guided Filtering

et al. 2022

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Detecting a moving pedestrian is still a challenging task in a smart surveillance system due to dynamic scenes. Locating and detecting the moving pedestrian simultaneously influences the development of an integrated but low-resource smart surveillance system. This paper proposes a novel approach to locating and detecting moving pedestrians in a video. Our proposed method first locates the region of interest (ROI) using a background subtraction algorithm based on guided filtering. This novel background subtraction algorithm allows our method to also filter unexpected noises at the same time, which could benefit the performance of our proposed method. Subsequently, the pedestrians are detected using YOLOv2, YOLOv3, and YOLOv4 within the provided ROI. Our proposed method resulted in more processing frames per second compared with previous approaches. Our experiments showed that the proposed method has a competitive performance in the CDNET2014 dataset with a fast-processing time. It costs around ~50 fps in CPU to classify moving pedestrians and maintain a highly accurate rate. Due to its fast processing, the proposed approach is suitable for IoT or smart surveillance device which has limited resource.INDEX TERMS moving object analysis; pedestrian localization and detection, convolutional neural network (CNN); integrated surveillance system; YOLO.

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Coarse-to-fine sample-based background subtraction for moving object detection

Cited by 15 publications

References 34 publications

Employing a multi-sensor fusion array to detect objects for an orbital transfer vehicle to remove space debris

Employing a multi-sensor fusion array to detect objects for an orbital transfer vehicle to remove space debris

Deep Learning Methods for Space Situational Awareness in Mega-Constellations Satellite-Based Internet of Things Networks

Moving Pedestrian Localization and Detection With Guided Filtering

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