scholarly journals A web-based application for face detection in real-time images and videos

2022 ◽  
Vol 2161 (1) ◽  
pp. 012071
Author(s):  
Mehul Arora ◽  
Sarthak Naithani ◽  
Anu Shaju Areeckal
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Face Detection ◽  
User Interfaces ◽  
Detection Algorithm ◽  
Frame Rate ◽  
Web Based ◽  
Detection Algorithms ◽  
Haar Cascade ◽  
The Web

Abstract Face detection is widely used in the consumer industry such as advertising, user interfaces, video streaming apps and in many security applications. Every application has its own demands and constraints, and hence cannot be fulfilled by a single face detection algorithm. In this work, we developed an interactive web-based application for face detection in real-time images and videos. Pretrained face detection algorithms, namely Haar cascade classifier, HOG-based frontal face detector, Multi-task Cascaded Convolutional Neural Network (MTCNN) and Deep Neural Network (DNN), were used in the web-based application. A performance analysis of these face detection algorithms is done for various parameters such as different lighting conditions, face occlusion and frame rate. The web app interface can be used for an easy comparison of different face detection algorithms. This will help the user to decide on the algorithm that suits their purpose and requirements for various applications.

scholarly journals Real time Face Detection and Optimal Face Mapping for Online Classes

2022 ◽  
Vol 2161 (1) ◽  
pp. 012063
Author(s):  
MCP Archana ◽  
CK Nitish ◽  
Sandhya Harikumar
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Face Detection ◽  
Local Binary Pattern ◽  
Online Classes ◽  
Web Based ◽  
Online Classrooms ◽  
Face Detection And Recognition ◽  
Haar Cascade ◽  
Improved Accuracy

Abstract The main objective of this paper is to provide a web-based tool for identifying faces in a real-time environment, such as Online Classes. Face recognition in real-time is now a fascinating field with an ever-increasing challenge such as light variations, occlusion, variation in facial expressions, etc. During the current pandemic scenario of COVID-19, the demand for online classrooms has rapidly increased. This has escalated the need for a real-time, economic, simple, and convenient way to track the attendance of the students in a live classroom. This paper addresses the aforementioned issue by proposing a real-time online attendance system. Two alternative face recognition algorithms are perceived in order to develop the tool for realtime face detection and recognition with improved accuracy. The algorithms adopted are Local Binary Pattern Histogram(LBPH) and Convolutional Neural Network (CNN) for face recognition as well as Haar cascade classifier with boosting for face detection. Experimental results show that CNN with an accuracy of 95% is better in this context than LBPH that yields an accuracy of 78%.

scholarly journals FastUAV-NET: A Multi-UAV Detection Algorithm for Embedded Platforms

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 724
Author(s):  
Amir Yavariabdi ◽  
Huseyin Kusetogullari ◽  
Turgay Celik ◽  
Hasan Cicek
Keyword(s):  
Real Time ◽  
Network Architecture ◽  
Computing Time ◽  
Detection Algorithm ◽  
Frame Rate ◽  
Detection Accuracy ◽  
Video Streams ◽  
Global Features ◽  
Detection Algorithms ◽  
Multi Uav

In this paper, a real-time deep learning-based framework for detecting and tracking Unmanned Aerial Vehicles (UAVs) in video streams captured by a fixed-wing UAV is proposed. The proposed framework consists of two steps, namely intra-frame multi-UAV detection and the inter-frame multi-UAV tracking. In the detection step, a new multi-scale UAV detection Convolutional Neural Network (CNN) architecture based on a shallow version of You Only Look Once version 3 (YOLOv3-tiny) widened by Inception blocks is designed to extract local and global features from input video streams. Here, the widened multi-UAV detection network architecture is termed as FastUAV-NET and aims to improve UAV detection accuracy while preserving computing time of one-step deep detection algorithms in the context of UAV-UAV tracking. To detect UAVs, the FastUAV-NET architecture uses five inception units and adopts a feature pyramid network to detect UAVs. To obtain a high frame rate, the proposed method is applied to every nth frame and then the detected UAVs are tracked in intermediate frames using scalable Kernel Correlation Filter algorithm. The results on the generated UAV-UAV dataset illustrate that the proposed framework obtains 0.7916 average precision with 29 FPS performance on Jetson-TX2. The results imply that the widening of CNN network is a much more effective way than increasing the depth of CNN and leading to a good trade-off between accurate detection and real-time performance. The FastUAV-NET model will be publicly available to the research community to further advance multi-UAV-UAV detection algorithms.

Field Assessment of Camera Based Drilling Dynamics

2021 ◽  
Author(s):  
Alexis Koulidis ◽  
Mohamed Abdullatif ◽  
Ahmed Galal Abdel-Kader ◽  
Mohammed-ilies Ayachi ◽  
Shehab Ahmed ◽  
...  
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Real Time ◽  
Tracking Algorithm ◽  
Interest Points ◽  
Stick Slip ◽  
Detection Algorithms ◽  
Point Tracking ◽  
Surface Data ◽  
Field Assessment

Abstract Surface data measurement and analysis are an established mean of detecting drillstring low-frequency torsional vibration or stick-slip. The industry has also developed models that link surface torque and downhole drill bit rotational speed. Cameras provide an alternative noninvasive approach to existing wired/wireless sensors used to gather such surface data. The results of a preliminary field assessment of drilling dynamics utilizing camera-based drillstring monitoring are presented in this work. Detection and timing of events from the video are performed using computer vision techniques and object detection algorithms. A real-time interest point tracker utilizing homography estimation and sparse optical flow point tracking is deployed. We use a fully convolutional deep neural network trained to detect interest points and compute their accompanying descriptors. The detected points and descriptors are matched across video sequences and used for drillstring rotation detection and speed estimation. When the drillstring's vibration is invisible to the naked eye, the point tracking algorithm is preceded with a motion amplification function based on another deep convolutional neural network. We have clearly demonstrated the potential of camera-based noninvasive approaches to surface drillstring dynamics data acquisition and analysis. Through the application of real-time object detection algorithms on rig video feed, surface events were detected and timed. We were also able to estimate drillstring rotary speed and motion profile. Torsional drillstring modes can be identified and correlated with drilling parameters and bottomhole assembly design. A novel vibration array sensing approach based on a multi-point tracking algorithm is also proposed. A vibration threshold setting was utilized to enable an additional motion amplification function providing seamless assessment for multi-scale vibration measurement. Cameras were typically devices to acquire images/videos for offline automated assessment (recently) or online manual monitoring (mainly), this work has shown how fog/edge computing makes it possible for these cameras to be "conscious" and "intelligent," hence play a critical role in automation/digitalization of drilling rigs. We showcase their preliminary application as drilling dynamics and rig operations sensors in this work. Cameras are an ideal sensor for a drilling environment since they can be installed anywhere on a rig to perform large-scale live video analytics on drilling processes.

Research and Implementation of Face Detection Based on OpenCV

2014 ◽  
Vol 971-973 ◽  
pp. 1710-1713
Author(s):  
Wen Huan Wu ◽  
Ying Jun Zhao ◽  
Yong Fei Che
Keyword(s):  
Face Recognition ◽  
Detailed Analysis ◽  
Real Time ◽  
Face Detection ◽  
Recognition System ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Accuracy Rate ◽  
Face Recognition System ◽  
The Face

Face detection is the key point in automatic face recognition system. This paper introduces the face detection algorithm with a cascade of Adaboost classifiers and how to configure OpenCV in MCVS. Using OpenCV realized the face detection. And a detailed analysis of the face detection results is presented. Through experiment, we found that the method used in this article has a high accuracy rate and better real-time.

New Trends on RIAs Development

2014 ◽  
pp. 323-336
Keyword(s):  
Web 2.0 ◽  
User Interfaces ◽  
Mobile Applications ◽  
Web Technology ◽  
Context Aware ◽  
Internet Applications ◽  
Web Based ◽  
Web 3.0 ◽  
Rich Internet Applications ◽  
The Web

Rich Internet Applications (RIAs) are considered one kind of Web 2.0 application; however, they have demonstrated to have the potential to transcend throughout the steps in the Web evolution, from Web 2.0 to Web 4.0. In some cases, RIAs can be leveraged to overcome the challenges in developing other kinds of Web-based applications. In other cases, the challenges in the development of RIAs can be overcome by using additional technologies from the Web technology stack. From this perspective, the new trends in the development of RIAs can be identified by analyzing the steps in the Web evolution. This chapter presents these trends, including cloud-based RIAs development and mashups-rich User Interfaces (UIs) development as two easily visible trends related to Web 2.0. Similarly, semantic RIAs, RMAs (Rich Mobile Applications), and context-aware RIAs are some of the academic proposals related to Web 3.0 and Web 4.0 that are discussed in this chapter.

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