scholarly journals Catwalkgrader: A Catwalk Analysis and Correction System using Machine Learning and Computer Vision

2021 ◽  
Vol 8 (3) ◽  
pp. 29-36
Author(s):  
Tianjiao Dong ◽  
Yu Sun
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Real Time ◽  
Professional Training ◽  
Keypoint Detection ◽  
Drastic Increase ◽  
Correction System ◽  
2D Model

In recent years, the modeling industry has attracted many people, causing a drastic increase in the number of modeling training classes. Modeling takes practice, and without professional training, few beginners know if they are doing it right or not. In this paper, we present a real-time 2D model walk grading app based on Mediapipe, a library for real-time, multi-person keypoint detection. After capturing 2D positions of a person's joints and skeletal wireframe from an uploaded video, our app uses a scoring formula to provide accurate scores and tailored feedback to each user for their modeling skills.

scholarly journals Cost-Effective CNNs for Real-Time Micro-Expression Recognition

2020 ◽  
Vol 10 (14) ◽  
pp. 4959
Author(s):  
Reda Belaiche ◽  
Yu Liu ◽  
Cyrille Migniot ◽  
Dominique Ginhac ◽  
Fan Yang
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Real Time ◽  
Video Clip ◽  
Cost Effective ◽  
Compact Representation ◽  
Expression Recognition ◽  
Memory Space ◽  
Computational Resources ◽  
Micro Expression

Micro-Expression (ME) recognition is a hot topic in computer vision as it presents a gateway to capture and understand daily human emotions. It is nonetheless a challenging problem due to ME typically being transient (lasting less than 200 ms) and subtle. Recent advances in machine learning enable new and effective methods to be adopted for solving diverse computer vision tasks. In particular, the use of deep learning techniques on large datasets outperforms classical approaches based on classical machine learning which rely on hand-crafted features. Even though available datasets for spontaneous ME are scarce and much smaller, using off-the-shelf Convolutional Neural Networks (CNNs) still demonstrates satisfactory classification results. However, these networks are intense in terms of memory consumption and computational resources. This poses great challenges when deploying CNN-based solutions in many applications, such as driver monitoring and comprehension recognition in virtual classrooms, which demand fast and accurate recognition. As these networks were initially designed for tasks of different domains, they are over-parameterized and need to be optimized for ME recognition. In this paper, we propose a new network based on the well-known ResNet18 which we optimized for ME classification in two ways. Firstly, we reduced the depth of the network by removing residual layers. Secondly, we introduced a more compact representation of optical flow used as input to the network. We present extensive experiments and demonstrate that the proposed network obtains accuracies comparable to the state-of-the-art methods while significantly reducing the necessary memory space. Our best classification accuracy was 60.17% on the challenging composite dataset containing five objectives classes. Our method takes only 24.6 ms for classifying a ME video clip (less than the occurrence time of the shortest ME which lasts 40 ms). Our CNN design is suitable for real-time embedded applications with limited memory and computing resources.

scholarly journals Real Time Face Recognition in Group Images using LBPH

2019 ◽  
Vol 8 (2) ◽  
pp. 1362-1367
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Face Recognition ◽  
Real Time ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Testing Dataset ◽  
Negative Images ◽  
The Face ◽  
Haar Cascade

Face recognition is a beneficial work in computer vision based applications. The goal of the proposed system is to provide complete face recognitions system capable of working a group of images. The faces are detected and verified the identity of an individual using a machine learning algorithm. The haar cascade detects the face from a group of images for training and testing dataset. The dataset contained positive and negative images for training and testing. The LBPH algorithm recognizes the faces from input images. The proposed system detects and recognizes faces with 98% accuracy

Context–aware assistive driving: an overview of techniques for mitigating the risks of driver in real-time driving environment

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shilpa Gite ◽  
Ketan Kotecha ◽  
Gheorghita Ghinea
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Real Time ◽  
Complete Analysis ◽  
Context Aware ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Content Type ◽  
Assistance Systems

Purpose This study aims to analyze driver risks in the driving environment. A complete analysis of context aware assistive driving techniques. Context awareness in assistive driving by probabilistic modeling techniques. Advanced techniques using Spatio-temporal techniques, computer vision and deep learning techniques. Design/methodology/approach Autonomous vehicles have been aimed to increase driver safety by introducing vehicle control from the driver to Advanced Driver Assistance Systems (ADAS). The core objective of these systems is to cut down on road accidents by helping the user in various ways. Early anticipation of a particular action would give a prior benefit to the driver to successfully handle the dangers on the road. In this paper, the advancements that have taken place in the use of multi-modal machine learning for assistive driving systems are surveyed. The aim is to help elucidate the recent progress and techniques in the field while also identifying the scope for further research and improvement. The authors take an overview of context-aware driver assistance systems that alert drivers in case of maneuvers by taking advantage of multi-modal human processing to better safety and drivability. Findings There has been a huge improvement and investment in ADAS being a key concept for road safety. In such applications, data is processed and information is extracted from multiple data sources, thus requiring training of machine learning algorithms in a multi-modal style. The domain is fast gaining traction owing to its applications across multiple disciplines with crucial gains. Research limitations/implications The research is focused on deep learning and computer vision-based techniques to generate a context for assistive driving and it would definitely adopt by the ADAS manufacturers. Social implications As context-aware assistive driving would work in real-time and it would save the lives of many drivers, pedestrians. Originality/value This paper provides an understanding of context-aware deep learning frameworks for assistive driving. The research is mainly focused on deep learning and computer vision-based techniques to generate a context for assistive driving. It incorporates the latest state-of-the-art techniques using suitable driving context and the driver is alerted. Many automobile manufacturing companies and researchers would refer to this study for their enhancements.

scholarly journals Real time Drowsy Driver Detection using Polynomial Kernel based Support Vector Machine

2021 ◽  
Vol 9 (9) ◽  
pp. 1389-1396
Author(s):  
Makhan Ahirwar
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Support Vector Machine ◽  
Real Time ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Human Errors ◽  
Drowsiness Detection ◽  
Non Linear ◽  
Data Separation

Abstract: Casualty increases from road accidents day by day. There are so many reasons that accident causes and mostly due to human errors. Driver drowsiness is one of them. A small drowsiness may turn it into a big accident that resulted heavy casualties. If any of the system automatically detects the driver’s drowsiness and alert at real time may secure many lives. Drowsiness can be recognized by different situations such as by opening full mouth, by closing both the eyes and a combination of both. This may advised not to drive at drowsy state. There are various techniques through which drowsiness can be detected at real time but accuracy matters. OpenCV is a highly utilized open source computer vision library through which facial features can be recognized effectively. Polynomial kernel based support vector machine (SVM) is an advanced classification technique through which drowsiness can be classified from face. SVM is advanced machine learning approach through which linear and non-linear data can be classified with higher level of accuracy. System pertained 96.17 % of accuracy. Polynomial kernel is useful for non-linear data separation. Here system classifies the expressional features of face and result accordingly for drowsiness detection. Keywords: Support Vector Machine (SVM), OpenCV, Machine Learning, Non-Linear SVM Model, Drowsiness Detection, Face Detection, Computer Vision.

scholarly journals [AI-Machine Learning] Optimized Sensorless Human Heartrate Estimation for a Dance Workout Application

2020 ◽  
Vol 5 (8) ◽  
pp. 888-892
Author(s):  
G. Jeong ◽  
N. Freitas
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Wearable Technology ◽  
Great Effort ◽  
The Real ◽  
Multiple Sensors ◽  
Correction System ◽  
Better Than ◽  
Fitness Application

Over the last decade, there has been a great effort to use technology to make exercise more interactive, measurable and gamified. However, in order to improve the accuracy of the detections and measurements needed, these efforts have always translated themselves into multiple sensors including purpose specific hardware, which results in extra expenses and induces limitations on the final mobility of the user. In this paper we aim to optimize a sensorless system that estimates the real-time user heartrate and performs better than the current wearable technology, for further calorie and other vital indicators calculations. The findings here will be applied on a posture correction system for a dance and fitness application.

Creating adaptive predictions for packaging-critical quality parameters using advanced analytics and machine learning

TAPPI Journal ◽  
2019 ◽  
Vol 18 (11) ◽  
pp. 679-689
Author(s):  
CYDNEY RECHTIN ◽  
CHITTA RANJAN ◽  
ANTHONY LEWIS ◽  
BETH ANN ZARKO
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Predictive Analytics ◽  
Paper Industry ◽  
Quality Parameters ◽  
Fast Change ◽  
The Face ◽  
Model Tuning ◽  
Reducing Costs ◽  
Time Critical

Packaging manufacturers are challenged to achieve consistent strength targets and maximize production while reducing costs through smarter fiber utilization, chemical optimization, energy reduction, and more. With innovative instrumentation readily accessible, mills are collecting vast amounts of data that provide them with ever increasing visibility into their processes. Turning this visibility into actionable insight is key to successfully exceeding customer expectations and reducing costs. Predictive analytics supported by machine learning can provide real-time quality measures that remain robust and accurate in the face of changing machine conditions. These adaptive quality “soft sensors” allow for more informed, on-the-fly process changes; fast change detection; and process control optimization without requiring periodic model tuning. The use of predictive modeling in the paper industry has increased in recent years; however, little attention has been given to packaging finished quality. The use of machine learning to maintain prediction relevancy under everchanging machine conditions is novel. In this paper, we demonstrate the process of establishing real-time, adaptive quality predictions in an industry focused on reel-to-reel quality control, and we discuss the value created through the availability and use of real-time critical quality.

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