High-definition Monitoring Research Articles

Attendance System Based on Face Recognition

Face recognition is one of the most important applications of image processing in the technical world. The process of identifying students using a face biostatistics system based on high-definition monitoring and other computer technologies is called a face recognition attendance system. A machine learning algorithm (Convolutional Neural Network) has been proposed to utilize the system in this paper. The identification of the human face is a current problem for verification purposes, particularly in the context of student attendance. The methods used today to take attendance are cumbersome and time-consuming. Manual recording makes it simple to alter attendance data. Both the current biometric methods and the conventional method of keeping attendance are susceptible to proxies. The development of this system aims to digitally replace the outdated method of taking attendance by calling names and keeping handwritten records. The proposed system for this research will help the students get enough time to focus on their studies, which can make them good contributors to our society.

Attendance System Based on Face Recognition

Face recognition is one of the most important applications of image processing in the technical world. The process of identifying students using a face biostatistics system based on high-definition monitoring and other computer technologies is called a face recognition attendance system. A machine learning algorithm (Convolutional Neural Network) has been proposed to utilize the system in this paper. The identification of the human face is a current problem for verification purposes, particularly in the context of student attendance. The methods used today to take attendance are cumbersome and time-consuming. Manual recording makes it simple to alter attendance data. Both the current biometric methods and the conventional method of keeping attendance are susceptible to proxies. The development of this system aims to digitally replace the outdated method of taking attendance by calling names and keeping handwritten records. The proposed system for this research will help the students get enough time to focus on their studies, which can make them good contributors to our society.

An Efficient Deep Learning-Based High-Definition Image Compressed Sensing Framework for Large-Scene Construction Site Monitoring.

High-definition images covering entire large-scene construction sites are increasingly used for monitoring management. However, the transmission of high-definition images is a huge challenge for construction sites with harsh network conditions and scarce computing resources. Thus, an effective compressed sensing and reconstruction method for high-definition monitoring images is urgently needed. Although current deep learning-based image compressed sensing methods exhibit superior performance in recovering images from a reduced number of measurements, they still face difficulties in achieving efficient and accurate high-definition image compressed sensing with less memory usage and computational cost at large-scene construction sites. This paper investigated an efficient deep learning-based high-definition image compressed sensing framework (EHDCS-Net) for large-scene construction site monitoring, which consists of four parts, namely the sampling, initial recovery, deep recovery body, and recovery head subnets. This framework was exquisitely designed by rational organization of the convolutional, downsampling, and pixelshuffle layers based on the procedures of block-based compressed sensing. To effectively reduce memory occupation and computational cost, the framework utilized nonlinear transformations on downscaled feature maps in reconstructing images. Moreover, the efficient channel attention (ECA) module was introduced to further increase the nonlinear reconstruction capability on downscaled feature maps. The framework was tested on large-scene monitoring images from a real hydraulic engineering megaproject. Extensive experiments showed that the proposed EHDCS-Net framework not only used less memory and floating point operations (FLOPs), but it also achieved better reconstruction accuracy with faster recovery speed than other state-of-the-art deep learning-based image compressed sensing methods.

A Super-resolution Reconstruction Method for Underwater Fish Images with Feedback Networks

<p>Video surveillance is commonly used for production process monitoring in aquaculture. Because underwater video images are prone to blurring and unclear detail texture, this paper proposes a super-resolution network reconstruction method with a feedback network. The reconstruction effect is enhanced by a feedback network in the process of mapping low-resolution images to high-resolution images, which is implemented with a constrained recurrent neural network (RNN) to process deep feedback information. However, the shallow features are fed to the feedback net-work module to generate deeper features through multiple upsampling and downsampling and progressive refinement, which give the network structure an early reconstruction capability and are conducive to more realistically reconstructing high-resolution images. A course learning strategy is introduced to make the network applicable to more complex tasks and improve its robust-ness. We construct a training set of 800 images and validation and test sets of 10 images each using images of marine net fish as the research object. The method of this paper is validated on the public dataset Set14 and a self-built dataset. The proposed method outperforms other methods in both subjective and objective evaluations on the public and self-built datasets. Good foundation for high-definition monitoring of aquaculture is laid. </p> <p> </p>

“Business as Usual,” But What Is Usual?

“Business as Usual,” But What Is Usual?

Enhancing Freeway Safety through Intervening in Traffic Flow Dynamics Based on Variable Speed Limit Control

New technologies and traffic data sources provide great potential to extend advanced strategies in freeway safety research. The High Definition Monitoring System (HDMS) data contribute comprehensive and precise individual vehicle information. This paper proposes an innovative Variable Speed Limit (VSL) based approach to manage crash risks by intervening in traffic flow dynamics on freeways using HDMS data. We first conducted an empirical analysis on real-time crash risk estimation using a binary logistic regression model. Then, intensive microscopic simulations based on AIMSUN were carried out to explore the effects of various intervention strategies with respect to a 3-lane freeway stretch in China. Different speed limits with distinct compliance rates under specified traffic conditions have been simulated. By taking into account the trade-off between safety benefits and delay in travel time, the speed limit strategies were optimized under various traffic conditions and the model with gradient feedback produces more satisfactory performance in controlling real-time crash risks. Last, the results were integrated into lane management strategies. This research can provide new ideas and methods to reveal the freeway crash risk evolution and active traffic management.