2022· International Journal of Modern Innovations and Emerging Trends· 0 citations
TL;DR
The paper explores how the classical approaches to image processing have been transformed to deep learning based methods such as their application in object detection and tracking, activity recognition, anomaly detection and facial recognition, and provides the future research direction, which is important to the next-generation intelligent surveillance systems.
Abstract
Modern security systems have raised a new system of a need to integrate automated surveillance systems as part of their security infrastructure because of the sudden increase in urbanization, civil safety issues and the necessity to have a smart monitoring system. The conventional surveillance systems are very dependent on human operator hence constraints include fatigue, delay in response and subjectivity. Computer vision as a branch of artificial intelligence will allow machines to read and understand visual information automatically, and thus change the traditional surveillance into its intelligent and active form. This paper gives an extensive research of the computer vision approaches in automated surveillance systems. It also explores how the classical approaches to image processing have been transformed to deep learning based methods such as their application in object detection and tracking, activity recognition, anomaly detection and facial recognition. System architectures, data acquisition pipelines, feature extraction methods, model training strategies and performance evaluation metrics are also discussed in the paper. Moreover, the issues like occlusion, change of illumination, scalability, privacy, real-time processing are examined. The effectiveness of the modern computer vision methods is discussed with references to the experimental results of the representative surveillance scenarios. Lastly, the paper provides the future research direction, such as edge-AI surveillance, multimodal fusion, and explainable computer vision, which is important to the next-generation intelligent surveillance systems.
The rapidly rising crime rates have necessitated advanced and automated security surveillance systems capable of robust, real-time detection, recognition, and prevention of crime. Although traditional surveillance systems have largely been deployed to enhance security and safety, they are inefficient, error-prone, and incapable of effectively processing and generating meaningful insights from the vast quantities of video data they produce. Further, they are adversely affected by extreme weather conditions and subject to human vandalism. The advent of deep learning has significantly transformed earlier automated crime detection, recognition, and prevention by enabling robust extraction and analysis of complex spatial and temporal features from surveillance videos. This research presents a comprehensive review and synthesis of various state-of-the-art deep learning architectures employed in surveillance video-based crime detection and recognition systems, including 3D Convolutional Neural Networks (3D-CNN), Residual Networks (ResNets), Recurrent Neural Networks (RNN), Bidirectional Long- and Short-Term Memory (BiLSTM), Gated Recurrent Units (GRUs), and the integration of attention mechanisms of Soft attention, hard attention, dual attention, and Multi-Head Self-Attention (MHSA). The study critically examines the architectures’ contributions to enhancing detection accuracy, recognition, and the capability to prevent crime. The review further highlights challenges associated with existing systems, including data scarcity, privacy concerns, computational complexity, data class imbalance, and limited real-world adoptability. The paper finally outlines emerging research gaps and future directions in the development of intelligent deep learning-based surveillance crime detection systems.
Traffic sign detection and recognition have become essential components of intelligent transportation systems and
Advanced Driver Assistance Systems (ADAS) due to the increasing need for road safety and automated driving. Conventional
traffic sign recognition approaches based on handcrafted features and traditional image processing techniques often struggle to
achieve high accuracy under varying environmental conditions such as poor lighting, occlusions, motion blur, and complex
backgrounds. To overcome these limitations, this work presents a Traffic Sign Detection and Recognition System Using
Convolutional Neural Networks (CNN), designed to accurately detect and classify traffic signs from input images. The proposed
framework utilizes computer vision techniques for image preprocessing, including resizing, normalization, and image
enhancement, followed by deep learning-based feature extraction and classification using a Convolutional Neural Network
(CNN). The CNN automatically learns discriminative visual features such as shapes, colors, and patterns from traffic sign
images, eliminating the need for manual feature engineering. The system is trained and evaluated using the German Traffic
Sign Recognition Benchmark (GTSRB) dataset, which contains more than 50,000 labeled images belonging to 43 different
traffic sign classes. A user-friendly interface is developed using Streamlit, enabling users to upload traffic sign images or capture
images through a webcam for real-time prediction. The trained model classifies the detected traffic sign and displays the
predicted class along with the confidence score. Experimental results are evaluated using Accuracy, Precision, Recall, F1-Score,
Confusion Matrix, and Training Performance Metrics, demonstrating the effectiveness of the proposed CNN-based framework
for accurate and reliable traffic sign recognition. The developed system contributes to improving road safety and can be
effectively integrated into intelligent transportation systems, driver assistance technologies, and autonomous vehicle
applications.
Yalla Lokesh Kumar, Dr. T. Siva Ramakrishna· International Journal for Re...· 0 citations
Intelligent surveillance systems need very dynamic visual intelligence that will recognise individuals and mark the presence of possible threats in real-time video surveillance. To overcome this requirement, a single deep learning system called SmartVision-AI is proposed, which combines face recognition and weapon identification in one feature-based architecture. The approach uses a dual-branch convolutional encoder, attention-based feature fusion, and multi-task learning, which is optimized towards low-latency CCTV systems. Tests on mixed-face and weapon image data sets show that the architecture can deliver face recognition accuracy of 96.8%, multi-class weapon detection accuracy of 94.7%, a false-positive rate drop to 21%, a precision increase of up to 19%, and a processing time of only 38 ms/frame, which can be effectively deployed in near real-time. Further processing indicates that there is an increase in temporal stability by 32% and a reduction in the use of GPU memory by 27% in comparison with individual task-specific models. The findings attest to the fact that SmartVision-AI provides a powerful, effective, and scalable intelligent threat-aware video surveillance system.
P.Shobana, V.Sai Shanmuga Raja, P. S. Rajakumar et al.· International journal of com...· 0 citations
This study proposes an Advanced Surveillance Framework that makes use of YOLOv10, a next-generation real-time object detection algorithm that greatly outperforms conventional single-sensor approaches in precision, recall, and real-time responsiveness.
Sadiya Begum, Lubna Nausheen, Ruqiya Fatima· International Journal of Eng...· 0 citations
Aims: This project presents the development and implementation of an AI-based traffic violation detection system designed to automate the monitoring of road traffic violations using CCTV footage and computer vision techniques. The aim is to create a comprehensive solution that can analyse real-time or recorded video streams to detect violations such as red-light running and speeding, thereby improving road safety and reducing the manual surveillance burden.
Study Design: The research employed a modular, data-driven approach combining software engineering best practices with advanced machine learning and computer vision techniques. The system was developed iteratively with a component-based architecture supporting separation of concerns and scalability.
Place and Duration of Study: The study was conducted at Babcock University, Department of Computer Science, School of Computing, Ilishan-Remo, Nigeria, from September 2023 to May 2024.
Methodology: The implementation utilised Python as the primary programming language, OpenCV for image processing and video analysis, and the YOLOv8 deep learning model for real-time object detection and vehicle classification. Multi-object tracking was accomplished using the DeepSORT algorithm, enabling consistent vehicle identification across video frames. Speed estimation was performed through frame displacement analysis and timestamp calculations. A rule-based violation detection module was integrated to identify traffic offences, including red-light violations and speeding. An Automatic Number Plate Recognition (ANPR) component utilising Optical Character Recognition (OCR) was included for vehicle identification. A structured MySQL database was implemented to store violation records with timestamps and evidence. An administrative dashboard, developed using HTML, CSS, JavaScript, and Bootstrap, provides real-time analytics, processing history, and violation reporting capabilities. The system architecture follows a three-tier model: Client-Side (Frontend), Server-Side (Backend), and Database Layer.
Results: Functional testing confirmed successful video upload, accurate vehicle detection using the YOLOv8 model, which demonstrated high precision, reliable multi-object tracking with consistent vehicle ID maintenance, accurate speed estimation through frame analysis, and correct violation flagging with real-time visual alerts. Performance evaluation demonstrated that the YOLOv8 model processed video frames efficiently at optimal detection speed, maintaining consistent detection accuracy and tracking reliability under various traffic conditions, including different lighting, weather, and traffic density scenarios. The system successfully generated violation reports with vehicle identification through licence plate recognition, timestamps, and evidence imagery for enforcement purposes.
Conclusion: The AI-based traffic violation detection system provides a practical and innovative solution to contemporary traffic violation control and management challenges. By integrating advanced computer vision features, including object detection, multi-object tracking, and automatic number plate recognition, the system analyses captured footage with high accuracy to identify violations and extract relevant vehicle details. The system's ability to provide real-time evidence capture and generate automated violation reports demonstrates how modern AI technologies can be effectively leveraged to create practical tools that meet contemporary traffic management needs, enabling authorities to manage road safety with greater effectiveness and efficiency.
Unknown authors· Asian Journal of Research in...· 0 citations
The design of a low-cost driver assistance system (DAS) using monocular camera input and artificial intelligence to enhance road awareness consists of using low-cost sensors instead of costly configurable sensors used in typical systems. The hybrid perception architecture of this system incorporates deep learning (via optimized YOLOv8) and traditional computer vision techniques to achieve high accuracy in detecting vehicles and pedestrians, which is consistent regardless of traffic conditions. Additionally, the hybrid lane detection algorithm combines edge-filtering techniques with geometric models to allow for lane detection in low-light or poorly marked lane conditions. Also, the development of a modular processing pipeline allows for real-time video preprocessing, feature extraction and risk assessment, therefore requiring less computational resources than standard DAS systems. Finally, testing showed that this DAS system consistently performs in real-time and achieves an acceptable degree of accuracy, irrespective of environmental conditions. The DAS system provides a common structure for a variety of vision techniques and can be scaled and constructed for a lower cost than most current DAS solutions, thereby facilitating the development of intelligent transportation systems and increasing access to transportation technology.
Poola Joshika, C. Dharshana, Shreya Sridharan et al.· 2026 6th International Confe...· 0 citations