Entropy feature and peak-means clustering based slowly moving object detection in head and shoulder video sequences

Sahoo, Prabodh Kumar; Kanungo, P.; Mishra, Satyasis; Mohanty, Bishnupad

doi:10.1016/j.jksuci.2020.12.019

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…In this paper, Haar features are used to train AdaBoost classifier, and scale of the obtained strong classifier is changed to accommodate different sizes of images to be detected, and the position of head and shoulder in the image is detected. [21][22][23] The Haar feature is the difference between the sum of pixels of two matrices. It is proposed by Paul Viola to calculate Haar features by means of integral graph, which improves the convenience of Haar feature calculations.…”

Section: Level 1 Haar + Adaboost Detection Schemementioning

confidence: 99%

Research on hierarchical pedestrian detection based on SVM classifier with improved kernel function

Zhang

2022

Measurement and Control

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The research of pedestrian target detection in complex scenes is still of great significance. Aiming at the problem of high missed detection rate and poor timeliness of pedestrian target detection in complex scenes. This paper proposes an improved classification method. First, Haar features were extracted from the images to be detected, and the candidate areas of pedestrians were determined by Adaboost classifier. Then, the traditional SVM classifier was improved by using the combined kernel function instead of the single kernel function, and the optimal proportion of each function in the combined kernel function was found by using the adaptive particle swarm optimization algorithm. Finally, the improved SVM classifier was combined with the fusion feature to further detect the candidate area to accurately locate the pedestrian’s position. Experimental results show that compared with the traditional detection framework, the proposed method can effectively improve the detection speed and the detection accuracy. This method has certain practical significance for pedestrian target detection in complex scenes.

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Section: Level 1 Haar + Adaboost Detection Schemementioning

confidence: 99%

Research on hierarchical pedestrian detection based on SVM classifier with improved kernel function

Zhang

2022

Measurement and Control

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“…To improve the image quality of actual-time video recording systems of video and CCTV, webcam coding standards were essential [16]. Performance depends on the approaches used to create video object planes using MPEG-4 encoding standards [17]. In general, objects move between frames at a much more rapid rate.…”

Section: Literature Surveymentioning

confidence: 99%

Machine Learning Approach for Prediction and Separation of Biodegradable and Non-biodegradable Waste

MENAKA.S

2022

Preprint

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Planners are faced with the enormous drawbacks of waste identification and removal. The amount of garbage and waste increased rapidly as a result of the growth in the urban population. In this research, they provide a physical approach based on a Deep Learning (DL) structure of waste segregation at the fundamental level. Unlike the recognition of objects of a certain entity, when images of objects entity have comparable qualities and attribute the trash could be shape, size, thing, color, or material. As a result, it is difficult to detect waste. The Improved Faster Recurrent Convolutional Neural Network (IFRCNN) method proposed the material based deep formation SmartBin could separate the trash non-biodegradable & biodegradable. The purpose of identifying garbage through proposed system used to quickly and efficiently classify garbage that is present in the bin. This proposed work aims to evaluate different IFRCNN for garbage classification VGG-16, InceptionNet, ResNet, and AlexNet, and train functionality alongside the hardware system used for garbage diagnosis in the bin. The proposed method performed best when compared with the InceptionNet Neural Network which had a precision of 98.15% and a loss of 0.10 for the training dataset and 96.23% and 0.13 for the validation data.

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Detecting Biodegradable and Non-biodegradable Image Using CNN and Deep Learning

Ripa,

Ahmed,

Akter

et al. 2024

Lecture Notes in Networks and Systems

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Entropy feature and peak-means clustering based slowly moving object detection in head and shoulder video sequences

Cited by 5 publications

References 17 publications

Research on hierarchical pedestrian detection based on SVM classifier with improved kernel function

Research on hierarchical pedestrian detection based on SVM classifier with improved kernel function

Machine Learning Approach for Prediction and Separation of Biodegradable and Non-biodegradable Waste

Detecting Biodegradable and Non-biodegradable Image Using CNN and Deep Learning

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