Recognition of classroom student state features based on deep learning algorithms and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Hu Jingchao ◽  
Haiying Zhang
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Subjective Evaluation ◽  
Recognition Algorithm ◽  
Video Data ◽  
State Recognition ◽  
Detection Model ◽  
State Classification ◽  
Intelligent Models ◽  
Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

scholarly journals Developing Traffic Congestion Detection Model Using Deep Learning Approach: A Case Study of Addis Ababa City Road

2020 ◽  
Author(s):  
Bedada Bekele ◽  
Kula Kekeba
Keyword(s):  
Deep Learning ◽  
Video Processing ◽  
Traffic Congestion ◽  
Addis Ababa ◽  
Video Data ◽  
High Rate ◽  
Detection Model ◽  
Video Footage ◽  
One Stage ◽  
Core Requirements

Abstract The traffic system is one of the core requirements of a civilized world and the development of the country depends on it in many aspects. In Ethiopia, the number of vehicles and pedestrians is increasing at a high rate from time to time. Excessive numbers of traffic on roads and improper control of traffic create traffic congestion. Uncontrolled traffic congestion hinders the transportation of goods and commuters from place to place and increases the volume of carbon emitted into the air. It can also either hampers or stagnates schedule, business, and commerce. Many images and video processing approaches have been researched in the literature on how to detect traffic congestion. One such approach is that of using background and foreground subtraction, convolutional neural network, and Average frame difference and deep learning method used to detect traffic congestion from different video sources. From the review one-stage object detector identified as the best methods to detect traffic congestion with acceptable accuracy and speed. In this study one-stage object detectors are used to detect traffic congestion from recorded video. Data is collected from different video footage and frames extracted from videos to prepare a dataset for the thesis. The extracted frames were labeled manually as congested and uncongested. To train, the models pre-trained weights were used. YOLOV3 and YOLOV5 model used for experimentation. Accuracy and speed metrics used to evaluate the performance of the models. A YOLOV3 model achieved 41.6 FPS and 68.6 % mAP on a testing dataset.

scholarly journals Semantic-Segmentation-Based Rail Fastener State Recognition Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Liming Li ◽  
Rui Sun ◽  
Shuguang Zhao ◽  
Xiaodong Chai ◽  
Shubin Zheng ◽  
...  
Keyword(s):  
Semantic Segmentation ◽  
Recognition Algorithm ◽  
Detection Methods ◽  
State Recognition ◽  
Detection Model ◽  
Practical Engineering ◽  
The One ◽  
And Function ◽  
Key Steps ◽  
Engineering Projects

Rail fastener status recognition and detection are key steps in the inspection of the rail area status and function of real engineering projects. With the development of and widespread interest in image processing techniques and deep learning theory, detection methods that combine the two have yielded promising results in practical detection applications. In this paper, a semantic-segmentation-based algorithm for the state recognition of rail fasteners is proposed. On the one hand, we propose a functional area location and annotation method based on a salient detection model and construct a novel slab-fastclip-type rail fastener dataset. On the other hand, we propose a semantic-segmentation-framework-based model for rail fastener detection, where we detect and classify rail fastener states by combining the pyramid scene analysis network (PSPNet) and vector geometry measurements. Experimental results prove the validity and superiority of the proposed method, which can be introduced into practical engineering projects.

scholarly journals A Study on Building a “Real-Time Vehicle Accident and Road Obstacle Notification Model” Using AI CCTV

2021 ◽  
Vol 11 (17) ◽  
pp. 8210
Author(s):  
Chaeyoung Lee ◽  
Hyomin Kim ◽  
Sejong Oh ◽  
Illchul Doo
Keyword(s):  
Deep Learning ◽  
Traffic Safety ◽  
Research Direction ◽  
Detection Algorithm ◽  
Obstacle Detection ◽  
Video Data ◽  
Future Research ◽  
The Road ◽  
Detection Model ◽  
On The Road

This research produced a model that detects abnormal phenomena on the road, based on deep learning, and proposes a service that can prevent accidents because of other cars and traffic congestion. After extracting accident images based on traffic accident video data by using FFmpeg for model production, car collision types are classified, and only the head-on collision types are processed by using the deep learning object-detection algorithm YOLO (You Only Look Once). Using the car accident detection model that we built and the provided road obstacle-detection model, we programmed, for when the model detects abnormalities on the road, warning notification and photos that captures the accidents or obstacles, which are then transferred to the application. The proposed service was verified through application notification simulations and virtual experiments using CCTVs in Daegu, Busan, and Gwangju. By providing services, the goal is to improve traffic safety and achieve the development of a self-driving vehicle sector. As a future research direction, it is suggested that an efficient CCTV control system be introduced for the transportation environment.

scholarly journals The Automatic Detection of Pedestrians under the High-Density Conditions by Deep Learning Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Cheng-Jie Jin ◽  
Xiaomeng Shi ◽  
Ting Hui ◽  
Dawei Li ◽  
Ke Ma
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Automatic Detection ◽  
High Density ◽  
Video Data ◽  
Pedestrian Flow ◽  
Detection Model ◽  
Deep Learning Algorithm ◽  
Crowd Density Estimation ◽  
Flow Experiments

The automatic detection and tracking of pedestrians under high-density conditions is a challenging task for both computer vision fields and pedestrian flow studies. Collecting pedestrian data is a fundamental task for the modeling and practical implementations of crowd management. Although there are many methods for detecting pedestrians, they may not be easily adopted in the high-density situations. Therefore, we utilized one emerging method based on the deep learning algorithm. Based on the top-view video data of some pedestrian flow experiments recorded by an unmanned aerial vehicle (UAV), we produce our own training datasets. We train the detection model by using Yolo v3, a very popular deep learning model among many available detection models in recent years. We find the detection results are good; e.g., the precisions, recalls, and F1 scores could be larger than 0.95 even when the pedestrian density is as high as 9.0   ped / m 2 . We think this approach could be used for the other pedestrian flow experiments or field data which have similar configurations and can also be useful for automatic crowd density estimation.

scholarly journals Research on Objective Evaluation of Recording Audio Restoration Based on Deep Learning Network

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Cong Jin ◽  
Wei Zhao ◽  
Hongliang Wang
Keyword(s):  
Deep Learning ◽  
Evaluation System ◽  
Evaluation Method ◽  
Subjective Evaluation ◽  
Audio Signal ◽  
Video Data ◽  
Learning Network ◽  
Deep Learning Network ◽  
Space Data ◽  
The Impact

There are serious distortion problems in the history audio and video data. In view of the characteristics of audio data repair, the intelligent technology of audio evaluation is explored. As the traditional audio subjective evaluation method requires a large number of personal to audition and evaluation, the tester’s subjective sense of hearing deviation and sample space data limited the impact of the accuracy of the experiment. Based on the deep learning network, this paper designs an objective quality evaluation system for historical audio and video data and evaluates the performance of the system and the audio signal quality from the perspective of feature extraction and network parameter selection. Experiments show that the system has good performance in this experiment; the predictive results and subjective evaluation of the correlation and dispersion indicators are good, up to 0.91 and 0.19.

scholarly journals Natural Language Description of Videos for Smart Surveillance

2021 ◽  
Vol 11 (9) ◽  
pp. 3730
Author(s):  
Aniqa Dilawari ◽  
Muhammad Usman Ghani Khan ◽  
Yasser D. Al-Otaibi ◽  
Zahoor-ur Rehman ◽  
Atta-ur Rahman ◽  
...  
Keyword(s):  
Natural Language ◽  
Feature Recognition ◽  
Scene Recognition ◽  
Video Data ◽  
Surveillance Video ◽  
Video Footage ◽  
Parallel Pipeline ◽  
September 11 Attacks ◽  
Description Framework ◽  
High Level

After the September 11 attacks, security and surveillance measures have changed across the globe. Now, surveillance cameras are installed almost everywhere to monitor video footage. Though quite handy, these cameras produce videos in a massive size and volume. The major challenge faced by security agencies is the effort of analyzing the surveillance video data collected and generated daily. Problems related to these videos are twofold: (1) understanding the contents of video streams, and (2) conversion of the video contents to condensed formats, such as textual interpretations and summaries, to save storage space. In this paper, we have proposed a video description framework on a surveillance dataset. This framework is based on the multitask learning of high-level features (HLFs) using a convolutional neural network (CNN) and natural language generation (NLG) through bidirectional recurrent networks. For each specific task, a parallel pipeline is derived from the base visual geometry group (VGG)-16 model. Tasks include scene recognition, action recognition, object recognition and human face specific feature recognition. Experimental results on the TRECViD, UET Video Surveillance (UETVS) and AGRIINTRUSION datasets depict that the model outperforms state-of-the-art methods by a METEOR (Metric for Evaluation of Translation with Explicit ORdering) score of 33.9%, 34.3%, and 31.2%, respectively. Our results show that our framework has distinct advantages over traditional rule-based models for the recognition and generation of natural language descriptions.

scholarly journals Deep learning-based automatic surgical step recognition in intraoperative videos for transanal total mesorectal excision

2021 ◽  
Author(s):  
Daichi Kitaguchi ◽  
Nobuyoshi Takeshita ◽  
Hiroki Matsuzaki ◽  
Hiro Hasegawa ◽  
Takahiro Igaki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Total Mesorectal Excision ◽  
Rectal Wall ◽  
Learning Model ◽  
Video Indexing ◽  
Video Data ◽  
Transanal Total Mesorectal Excision ◽  
Mesorectal Excision ◽  
Purse String ◽  
Deep Learning Model

Abstract Background Dividing a surgical procedure into a sequence of identifiable and meaningful steps facilitates intraoperative video data acquisition and storage. These efforts are especially valuable for technically challenging procedures that require intraoperative video analysis, such as transanal total mesorectal excision (TaTME); however, manual video indexing is time-consuming. Thus, in this study, we constructed an annotated video dataset for TaTME with surgical step information and evaluated the performance of a deep learning model in recognizing the surgical steps in TaTME. Methods This was a single-institutional retrospective feasibility study. All TaTME intraoperative videos were divided into frames. Each frame was manually annotated as one of the following major steps: (1) purse-string closure; (2) full thickness transection of the rectal wall; (3) down-to-up dissection; (4) dissection after rendezvous; and (5) purse-string suture for stapled anastomosis. Steps 3 and 4 were each further classified into four sub-steps, specifically, for dissection of the anterior, posterior, right, and left planes. A convolutional neural network-based deep learning model, Xception, was utilized for the surgical step classification task. Results Our dataset containing 50 TaTME videos was randomly divided into two subsets for training and testing with 40 and 10 videos, respectively. The overall accuracy obtained for all classification steps was 93.2%. By contrast, when sub-step classification was included in the performance analysis, a mean accuracy (± standard deviation) of 78% (± 5%), with a maximum accuracy of 85%, was obtained. Conclusions To the best of our knowledge, this is the first study based on automatic surgical step classification for TaTME. Our deep learning model self-learned and recognized the classification steps in TaTME videos with high accuracy after training. Thus, our model can be applied to a system for intraoperative guidance or for postoperative video indexing and analysis in TaTME procedures.

scholarly journals Machine Learning-Based Malicious X.509 Certificates’ Detection

2021 ◽  
Vol 11 (5) ◽  
pp. 2164
Author(s):  
Jiaxin Li ◽  
Zhaoxin Zhang ◽  
Changyong Guo
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Ensemble Learning ◽  
Traffic Analysis ◽  
Learning Models ◽  
Detection Model ◽  
Analysis Tools ◽  
Average Accuracy ◽  
Machine Learning Models

X.509 certificates play an important role in encrypting the transmission of data on both sides under HTTPS. With the popularization of X.509 certificates, more and more criminals leverage certificates to prevent their communications from being exposed by malicious traffic analysis tools. Phishing sites and malware are good examples. Those X.509 certificates found in phishing sites or malware are called malicious X.509 certificates. This paper applies different machine learning models, including classical machine learning models, ensemble learning models, and deep learning models, to distinguish between malicious certificates and benign certificates with Verification for Extraction (VFE). The VFE is a system we design and implement for obtaining plentiful characteristics of certificates. The result shows that ensemble learning models are the most stable and efficient models with an average accuracy of 95.9%, which outperforms many previous works. In addition, we obtain an SVM-based detection model with an accuracy of 98.2%, which is the highest accuracy. The outcome indicates the VFE is capable of capturing essential and crucial characteristics of malicious X.509 certificates.

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