A Review on Microstrip Patch Antenna Performance Improvement Techniques on Various Applications
Volume-3 | Issue-3
A Review on Finding Efficient Approach to Detect Customer Emotion Analysis using Deep Learning Analysis
Volume-3 | Issue-2
A Comparative Analysis of Prediction of Student Results Using Decision Trees and Random Forest
Volume-4 | Issue-3
Study of Security Mechanisms to Create a Secure Cloud in a Virtual Environment with the Support of Cloud Service Providers
Volume-2 | Issue-3
Construction of Black Box to Detect the Location of Road Mishap in Remote Area in the IoT Domain
Volume-3 | Issue-2
Fault Diagnosis in Hybrid Renewable Energy Sources with Machine Learning Approach
Volume-3 | Issue-3
Secure and Optimized Cloud-Based Cyber-Physical Systems with Memory-Aware Scheduling Scheme
Volume-2 | Issue-3
Stochastic Geometry and Performance Analysis of Large Scale Wireless Networks
Volume-3 | Issue-3
Computer Vision on IOT Based Patient Preference Management System
Volume-2 | Issue-2
Fake News Detection using Data Mining Techniques
Volume-3 | Issue-4
A Review on Microstrip Patch Antenna Performance Improvement Techniques on Various Applications
Volume-3 | Issue-3
Fake News Detection using Data Mining Techniques
Volume-3 | Issue-4
A Comparative Analysis of Prediction of Student Results Using Decision Trees and Random Forest
Volume-4 | Issue-3
Speedy Detection Module for Abandoned Belongings in Airport Using Improved Image Processing Technique
Volume-3 | Issue-4
Deployment of Artificial Intelligence with Bootstrapped Meta-Learning in Cyber Security
Volume-4 | Issue-3
Design an Early Detection and Classification for Diabetic Retinopathy by Deep Feature Extraction based Convolution Neural Network
Volume-3 | Issue-2
Design of an Intelligent Approach on Capsule Networks to Detect Forged Images
Volume-3 | Issue-3
Future Challenges of the Internet of Things in the Health Care Domain - An Overview
Volume-3 | Issue-4
Construction of Black Box to Detect the Location of Road Mishap in Remote Area in the IoT Domain
Volume-3 | Issue-2
A Review on Finding Efficient Approach to Detect Customer Emotion Analysis using Deep Learning Analysis
Volume-3 | Issue-2
Volume - 6 | Issue - 1 | march 2024
Published
24 February, 2024
The importance of developing automated video surveillance systems for public safety and security, particularly in crime analysis, has witnessed significant growth in recent years. This survey delves into the current landscape of automated video surveillance systems, emphasizing advancements in crime analysis and exploring existing methodologies and technologies. The study underscores the significance of employing deep learning models in video analysis. Furthermore, the study suggests a deep learning architecture to address the challenges of the existing methods. The goal of the suggested approach is to help security and law enforcement organizations quickly react to any dangers by precisely identifying unusual occurrences or actions in video sequences. The DenseNet-121 architecture is used for efficient spatial and temporal data acquisition from the video frames. This architecture is characterized by a dense connection structure in which all levels get feature mappings from all layers before them. The characteristics of DenseNet-121 can help in the accurate identification of anomalies in video streams and differentiate between normal and abnormal actions. In addition, the study also delves into the topic of using a cell structure with varied sizes to effectively split video sequences. This allows for flexible analysis and can accommodate different sorts of abnormalities. Anomaly detection accuracy can be further improved by adding size, motion, and location information to prediction and measurement models. This study serves as a foundation for the future research that aims to develop a more robust and efficient automated video surveillance solutions.
KeywordsVideo Anomaly Detection Public Safety Security Crime Analysis Densenet-121
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