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Intrusion Detection from Surveillance video Using Frame Based image processing Techniques 1st

Intrusion Detection from Surveillance Video Using Frame Base...

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1st International Research conference on Computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Mohamed Yaseen, M.S. Radeesh, P. Chitra, P. Ramesh, Kishore SRM Institute of Science and Technology Vadapalani Tamil Nadu Chennai India

ISBN: (纸本)9783031823824

In India, the number of surveillance cameras has increased rapidly, making it challenging for law enforcement to go through the vast amount of recorded footage. We suggest a technique for quickly reviewing large amounts of video feed in order to address this issue. By utilizing sophisticated algorithms such as facial recognition, lightweight YuNet model face detection, and motion detection through background subtraction, our system accelerates the analysis of videos from various camera systems. Our model is a great tool for any agency looking to improve its operational efficiency when handling large amounts of monitoring data because it can quickly identify important events and produce concise summaries with timestamps. This piece of research contributes towards development within this field since such new methods are combined into one single solution thereby advancing surveillance technology while at the same time providing concrete benefits for everyday challenges faced by law enforcement agencies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Intrusion detection

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Mobility Anomaly Detection with Intelligent video Surveillance 28th

Mobility Anomaly Detection with Intelligent Video Surveillan...

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28th International conference on image processing, Computer Vision, and Pattern Recognition, IPCV 2024, and 23rd International conference on Information and Knowledge Engineering, IKE 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

作者： Ebrahimi, Fatemeh Rousseau, Jacqueline Meunier, Jean Department of Computer Science and Operations Research University of Montreal Montreal Canada School of Rehabilitation University of Montreal Montreal Canada

ISBN: (纸本)9783031859328

This paper outlines a study aimed at enhancing elderly care through an intelligent video surveillance system that leverages deep learning for detecting mobility anomalies, specifically near-falls. Identifying near-falls is essential because people who experience frequent near-falls while carrying out their daily activities are at risk of future falls. We successfully developed an autoencoder to detect these anomalies, particularly near-falls, by identifying high reconstruction errors throughout five consecutive frames. To extract a person's skeleton, we utilized MoveNet and narrowed it down to only seven keypoints. We then used a set of 20 features, encompassing joint positions, velocities, accelerations, angles, and angular accelerations, to train the model. Our model was tested on 100 videos of simulated daily activities recorded in an apartment laboratory, where 50 of them contained a near-fall. Results show that our model can successfully detect near-falls with 90% sensitivity, specificity, and accuracy, highlighting its potential to enhance elderly care in their living environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Fall detection

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Experimental Study on Assistive System Using Statistical Signal processing Technology for Visually Impaired People 10th

Experimental Study on Assistive System Using Statistical Sig...

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10th International conference on Health Informatics and Medical Systems, HIMS 2024 and 10th International conference on Biomedical Engineering, BIOENG 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

作者： Shimizu, Shunji Hori, Masaya Inoue, Hiroaki Miwakeichi, Fumikazu Hirai, Nobuhide Kikuchi, Senichiro Kato, Satoshi Department of Applied Information Engineering Suwa University of Science Chino Japan Department of Engineering and Management Suwa University of Science Chino Japan Shimizu Research Laboratory Suwa University of Science Chino Japan Department of Interdisciplinary Statistical Mathematics The Institute of Statistical Mathematics Tachikawa Japan Health Administration Center Tokyo Medical and Dental University Bunkyo Japan Department of Rehabilitation Sciences Gunma University Maebashi Japan Oyama Fujimidai Hospital Shimotsuke Japan

ISBN: (纸本)9783031859076

In this study, we will investigate a new image signal processing method in order to develop a highly useful walking support device for visually impaired people. Visually impaired people walk independently using assistive devices. Currently, there are many support devices that use object detection and object recognition using deep learning, but there are still no highly useful devices. The smallest unit constituting an image signal is a pixel. Adjacent pixels are correlated with each other. The positional relationship of pixels expresses the features of objects, and scenery in the image. Autoregressive (AR) model is a regression analysis method used to analyze time-series signals. AR models are one of the basic methods for signal processing for speech recognition and control. Furthermore, in image signal processing, it can be used as a signal processing method when data has some kind of order, such as texture feature extraction, contour processing, and video processing. In this experiment, we converted the pixel array of the image signal into a one-dimensional time-series signal and process the signal. We tried to identify objects or regions within images. The method proposed in this study may be used to extract "Point of Interest" (POI) and "Point of Reference" (POR), which are important information for visually impaired people. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image texture

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Deep Fake video Detection Using Transfer Learning Techniques 6th

Deep Fake Video Detection Using Transfer Learning Techniques

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6th International conference on Advanced Informatics for Computing Research, ICAICR 2023

作者： Anuhya, Karampuri Shivaram, D. Karthikeya, Deekonda Jyothi, B.N. Jabbar, M.A. Akhtar, Zahid Vardhaman College of Engineering Kacharam Shamshabad India State University of New York Polytechnic Institute Marcy United States

ISBN: (纸本)9783031840586

Deep Fakes are now a concern and a threat to society as they are mostly misused for creating chaos and are tools of crimes. There are many software that can create the most realistic deep fakes in a matter of seconds using image processing and deep learning techniques. This work deals with and adapts transfer learning techniques for the detection of deep fake videos. It also proposes the ensemble model advantages for better judgment. The FaceForensics++ dataset which consists of FaceSwap, Face2Face, NeuralTextures, DeepFakes, and small portions of them are used for training three different transfer learning models VGG19, EfficientNetB5, and InceptionResNetV2. The ensemble model of these is used to finally communicate the results. The results obtained individual model-wise as well as ensemble of the same has been observed as part of this work. The detection accuracy of InceptionResNetV2 is 96.74% whereas the ensemble model is 65% for the same samples and training parameters. The results of our work indicate that adaptation of transfer learning is a good sign for deep fake video detection and it performs better when trained with large amounts of heterogenous datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Comparison of On-Board and Off-Board processing Power Consumption for Drone Camera images 16th

Comparison of On-Board and Off-Board Processing Power Cons...

引用

16th International conference on ICT Innovations, ICT Innovations 2024

作者： Mitrev, Atanasko Boris Stojkoska, Biljana Risteska Faculty of Computer Science and Engineering Ss. Cyril and Methodius University in Skopje Skopje Macedonia

ISBN: (纸本)9783031861611

Unmanned aerial vehicles (UAVs) equipped with cameras, capture images and videos from high altitude, usually for surveillance purpose. This paper investigates the power consumption of a drone processing unit under two different operational scenarios: (i) On-Board (local image processing using an onboard Raspberry Pi) and (ii) Off-Board (streaming video over Wi-Fi for remote processing). Therefore, we deployed the well-known YOLO (You Only Look Once) image detection algorithm on the Raspberry Pi and investigated the power consumption for the both scenarios. Contrary to the expectations, our findings prove that energy consumption is very similar in both scenarios. The results of this study are valuable for designers of energy-efficient UAV systems, particularly for applications where prolonged flight duration and real-time data processing are critical. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Drones

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1st International conference on Computational Technologies and Electronics, ICCTE 2023

1st International Conference on Computational Technologies a...

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1st International conference on Computational Technologies and Electronics, ICCTE 2023

ISBN: (纸本)9783031819346

The proceedings contain 27 papers. The special focus in this conference is on Computational Technologies and Electronics. The topics include: Wildlife Detection Using ANN and Other Modern Technology: A Survey of Literatures;enhancing Learning Outcomes Through the Use of Conducive Learning Spaces;smart Diagnosis Using Symptoms for Seeking a Specialist Doctor;a Comprehensive Review and Future Prospects of Lie Detection Using Machine Learning;searching Optimizers for Deep Learning Based Hyperspectral image Classification;efficient Crop Recommendation System: A Machine Learning Based Approach;Human Fall Detection Using Transfer Learning-Based 3D CNN;anomaly Detection in Respiratory Events Using Machine Learning;federated Learning to Speed Up Pre-processing of Large Data Sets;flood Susceptibility Zonation Using Geospatial Frequency Ratio and Artificial Neural Network Techniques within Himalayan Terai Region: A Comparative Exploration;a Fertilizer Recommendation System Using an Assembly of Regressors Coupled with Nature-Inspired Optimization Algorithms;state-of-the-Art in Feature Selection: Applications of the Slime Mould Algorithm;MOODBYTBLB: Impact of Covid-19 Among Indians: A Sentiment Analysis Using Textblob;mythBuster: A Comparative Analysis of Few Machine Learning and Deep Learning Models for Fake News Detection;unsupervised Approach for Word Sense Disambiguation in Bengali;a Hybrid Method for Bengali Word Segmentation from Handwritten Copies of School Students;securing Social Spaces: Harnessing Deep Learning to Eradicate Cyberbullying;exploring Intonation Patterns in Nepali Speech: A Phonetic and Linguistic Analysis for Text-to-Speech System;sentiment Analysis on Airline Customer Review Using Language Model and Capsule Network;implementation of Digital Healthcare for Improving Maternal Care: A Systematic Review;video Content Analysis and Classification Based on Human Activity Recognition;Machine Learning Based Expert System for Breast Cancer Prediction (MLESBCP).

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12th CCF conference on BigData, BigData 2024

12th CCF Conference on BigData, BigData 2024

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12th CCF conference on BigData, BigData 2024

ISBN: (纸本)9789819610235

The proceedings contain 26 papers. The special focus in this conference is on BigData. The topics include: A Multi-dimensional Early Warning Mechanism for Biological Invasions: A Case Study of Vespa Mandarinia;DLP-FR: Learning Predictable Degradation for Robust Blind Face Restoration;A Joint Relation Extraction Model Utilizing Message Passing Mechanism for Feature Fusion to Alleviate FN Problem from Distant Supervision;urban Traffic Management: A Predictive Approach Using Mobile Phone Data;FedPSED: Federated Public Safety Emergency Detection Based on Adaptive Aggregation Strategy;evaluation of Retrieval-Augmented Generation: A Survey;CMNet: Fast Time Series Forecasting Based on Hybrid Convolution-MLP Architecture;a Domain Adaptive Based Reinforcement Learning Algorithm for Job Shop Scheduling Problems;Large-Scale Data Generation Using SWLBM on the Sunway TaihuLight Supercomputer and Subsequent Data Mining with Physics-Informed Neural Networks;Enhancing Interaction Graph of Data Schema and Syntactic Structure with Pre-trained Language Model for Text-to-SQL;spatial Evolution Analysis of the Level of Digital Economy Development in China;predicting Calibrated Conversion Rate of Online Advertising Using a Multi-task Mixture-of-Experts Calibration Model;a Transformer-Based Spatio-Temporal Graph Neural Network for Anomaly Detection on Dynamic Graphs;morphological Semantic Ensemble Filtering of Massive Sentence Pairs for Neural Machine Translation;improving Event-Level Financial Sentiment Analysis with Retrieval-Augmented Multipath Chain-of-Thought Prompting;POSRho: Efficient Spearman’s Rho Calculation for Big Data;a Fusion Tuning Method for Named Entity Recognition;Design of AXI Bus-Based IP Core for image processing;automated Clinical Summary Generation via Integrating Structured and Unstructured Data;a Review on Deep Learning for Sequential Recommender Systems: Key Technologies and Directions;TrajUT: Intruder Trajectory Recovery on Utility Tunnel via video Surveillance S

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1st International conference on Computational Technologies and Electronics, ICCTE 2023

1st International Conference on Computational Technologies a...

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1st International conference on Computational Technologies and Electronics, ICCTE 2023

ISBN: (纸本)9783031819803

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Fine-Grained Spatial-Temporal Framework for Engagement Prediction 2nd

Fine-Grained Spatial-Temporal Framework for Engagement Pred...

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2nd CSIG conference on Emotional Intelligence, CEI 2024

作者： Wang, Shanshan Zheng, Yong Wang, Keyang Zhang, Xingyi The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Beijing China Institutes of Physical Science and Information Technology Anhui University Anhui China Zhejiang Dahua Technology Co. Ltd. Zhejiang China School of Computer Science and Technology Anhui University Anhui China

ISBN: (纸本)9789819650835

The engagement prediction task aims to identify the current level of involvement of individuals based on the information presented in video clips. In recent years, engagement prediction has attracted considerable attention due to its significance in real-life scenarios. We observed that an individual’s engagement status is often primarily manifested in the facial region. This observation prompted us to solve the engagement prediction problem by exploiting facial information to learn features related to engagement. In this paper, we propose a novel model for learning fine-grained features, called Fine-Grained Spatial-Temporal Framework (FGST). Specifically, we consider both spatial and temporal aspects. In the spatial domain, we propose an entropy-weighted method based on Fourier transform. By applying Fourier transform to video frames and introducing the concept of information entropy, we can segregate engagement-related information from engagement-unrelated information and mining difficult samples. This aids in enabling the model to learn features conducive to engagement prediction. In the temporal domain, we calculate the differences between adjacent frames and sum these differences to obtain regions of pixel variation between video frames, thereby generates saliency weight and improving model performance. Experiments on several public datasets demonstrate the proposed model can outperform the state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fourier transforms

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Architectural implications for high-quality video format conversion

Architectural implications for high-quality video format con...

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conference on image and video communications and processing 2005

作者： Bellers, EB Janssen, JGWM Philips Semicond San Jose CA USA

ISBN: (纸本)0819456586

video format conversion is required if the received video format mismatches the display format (spatially and/or temporally). In addition, a high-quality video format converter can successfully be used to eliminate movie udder (2:2 and 3:2 pull down) by applying motion-compensated temporal up-conversion techniques which provides a smooth motion portrayal. In this paper we present our architecture, the associated algorithms which provide high-quality video format conversion, and there mutual implications.

关键词： video Motion estimation Digital signal processing video processing Detection and tracking algorithms Consumer electronics Motion analysis Silicon image processing Motion detection

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