Recognition of dynamic objects from UGVs using Interconnected Neural network-based Computer Vision system

Gökçe, Barış; Sonugür, Güray

doi:10.1080/00051144.2022.2031539

Cited by 6 publications

(2 citation statements)

References 37 publications

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“…The maximum success percentage for object classification in experimental investigations was 96.1%. The acquired findings are compared to the currently popular YOLO object identification technique [20].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Performance Analysis of Video Object Detection and Classification Using Modified Yolov3 and Mobilenet Algorithm

Mohandoss

2023

JMC

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Detecting foreground objects in video is crucial in various machine vision applications and computerized video surveillance technologies. Object tracking and detection are essential in object identification, surveillance, and navigation approaches. Object detection is the technique of differentiating between background and foreground features in a photograph. Recent improvements in vision systems, including distributed smart cameras, have inspired researchers to develop enhanced machine vision applications for embedded systems. The efficiency of featured object detection algorithms declines as dynamic video data increases as contrasted to conventional object detection methods. Moving subjects that are blurred, fast-moving objects, backdrop occlusion, or dynamic background shifts within the foreground area of a video frame can all cause problems. These challenges result in insufficient prominence detection. This work develops a deep-learning model to overcome this issue. For object detection, a novel method utilizing YOLOv3 and MobileNet was built. First, rather than picking predefined feature maps in the conventional YOLOv3 architecture, the technique for determining feature maps in the MobileNet is optimized based on examining the receptive fields. This work focuses on three primary processes: object detection, recognition, and classification, to classify moving objects before shared features. Compared to existing algorithms, experimental findings on public datasets and our dataset reveal that the suggested approach achieves 99% correct classification accuracy for urban settings with moving objects. Experiments reveal that the suggested model beats existing cutting-edge models by speed and computation.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Based Performance Analysis of Video Object Detection and Classification Using Modified Yolov3 and Mobilenet Algorithm

Mohandoss

2023

JMC

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“…En çok kullanılan yöntemlerin başında Gauss Karışımı ve derin öğrenme destekli Gauss Karışımı temelli yöntemler gelmektedir. Li 2014, Feng et al 2017), Kullanılan diğer yöntemler arasında; eğiticisiz öğrenme modellerinden bulanık mantık temelli (Acampora et al 2015) ve Öz Örgütlenmeli Sinir Ağları (Johnson and Hogg 1996), Evrişimsel Sinir Ağı (ESA) modelleri (Shifu et al 2016), Yapay Sinir Ağları modelleri (Gökçe and Sonugür 2022), Zaman Gecikmeli Sinir Ağları (Yang and Ahuja 1998) ve Gizli Markov Model tabanlı yöntemler (Acharya and Gantayat 2015) sayılabilir. İnsan hareketini algılama konusunda, Akdağ (2015) 2 boyutlu hareket algılama tekniğini kullanmıştır.…”

Section: Introductionunclassified

Towards Real-Time Human Behavior Understanding: A Suboptimal Shape Descriptor

Sonugür

ÇAKI

AKAN

et al. 2022

Afyon Kocatepe University Journal of Sciences and Engineering

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In this study, two novel shape descriptors are proposed to be used in human behavior understanding problem. First is optimal shape descriptor, which has high performance but works very slow due to high algorithmic complexity. Second is suboptimal shape descriptor, performance of which is very close to optimal one, but works much more faster. Optimal means using minimum data to represent maximum knowledge. Algorithms are run on Weizmann dataset and results are shown both as figure and video link. Classification was performed using 12 statistical features extracted from the data sets' human silhouettes. An accuracy rating of 92 percent was obtained by using Euclidean distance in classification.

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Multi-sensor based strategy learning with deep reinforcement learning for unmanned ground vehicle

Luo

2023

International Journal of Intelligent Networks

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Recognition of dynamic objects from UGVs using Interconnected Neural network-based Computer Vision system

Cited by 6 publications

References 37 publications

Machine Learning Based Performance Analysis of Video Object Detection and Classification Using Modified Yolov3 and Mobilenet Algorithm

Machine Learning Based Performance Analysis of Video Object Detection and Classification Using Modified Yolov3 and Mobilenet Algorithm

Towards Real-Time Human Behavior Understanding: A Suboptimal Shape Descriptor

Multi-sensor based strategy learning with deep reinforcement learning for unmanned ground vehicle

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