By Edwin Sam
In recent times, the multiplication of crime rates in metropolitan areas has become a growing concern. The process of monitoring accidents is still done manually, consuming significant time and resources. As it is challenging to maintain manned security in large spaces, a proposed solution involving machine learning technology could be the way forward. Emergency Alerts Using Machine Learning
Motivation
The rising road accidents in metropolitan areas necessitate advanced security systems for civil protection and competent law enforcement. Automation is crucial to handle the vast amount of data generated by video surveillance systems, reducing the need for manual monitoring and potential human errors.
Objectives
The objectives of this project include conducting literature reviews, understanding CNN model architectures, data collection and pre-processing, exploring and experimenting with various machine learning models, designing and implementing a model for road accident detection, and evaluating model performance.
Literature Review
In the field of machine learning, researchers have made significant advancements in detecting crime over the past decade. Li et al. proposed a lightweight action recognition architecture using deep neural networks [2]. Nejdet et al. developed an intelligent traffic accident detection system based on the exchange of micro vehicle sizes [3].
Research Methodology
The research methodology involves studying various pre-trained image classification CNN models like VGG-16, ResNet, Inception-V3, and MobileNet. The data collection process includes isolating 25,000 labeled videos from multiple sources to train separate models for accident detection. Image classification involves predicting categories for a set of labeled images using a learning algorithm.
Understanding Image Classification
Image classification presents challenges such as viewpoint changes, scale changes, image deformation, and background noise. The data-driven approach uses a training dataset to teach the computer to recognize visual patterns and classify images.
Convolutional Neural Networks CNNs are commonly used for image classification, allowing for local understanding of images, reducing training time, and requiring less data. They consist of convolutional layers, pooling layers, ReLU modification layers, and fully connected layers.
Understanding the Architecture of CNN CNNs employ convolutional layers, where a window slides across the image to perform weighted sums of pixel values. The model’s beauty lies in the reduced number of parameters compared to fully connected networks, making them efficient and adaptable.
Pre-processing
Pre-processing involves splitting the video into frames, selecting frames at a specific frame rate, augmenting and resizing frames, and normalizing RGB values.
MODEL ARCHITECTURE FOR ROAD ACCIDENTS
The road accident detection model focuses on predicting potential collisions among vehicles in close proximity.
MODEL CREATION FOR ROAD ACCIDENT DETECTION
The model uses VGG-16 as the base model and is trained on a dataset containing accident and non-accident videos in a 2:1 ratio. The goal is to achieve high recognition value to minimize missed accidents and potential fatalities.
CHOOSING PRE-TRAINED MODELS
The pre-trained model VGG-16 is selected as it provided the highest accuracy on the dataset. Comparative analysis was conducted between various pre-trained models to identify the best fit.
USER’S VIEW
The system is developed for safety purposes in residential areas, allowing users to connect CCTV cameras to the system. Live video is processed through the trained model, and automatic alerts are sent to relevant authorities in the event of a road accident.
WORKING OF THE SYSTEM
The system captures live streams from CCTV cameras, breaks the video stream into frames, and passes them through the trained model for accident detection. Alerts are then generated and sent to the concerned personnel.
RESULT ANALYSIS
The model achieved an accuracy of 88.5% and a recall of 99.4% for road accident detection.
Conclusion
The proposed machine learning-based real-time CCTV surveillance patrol system addresses the rising crime rates and the need for enhanced security in metropolitan areas. By automating the accident detection process, it reduces manual monitoring and the risk of human errors. The system’s accuracy and efficiency make it a promising solution for public safety and law enforcement.
Future Scope The system can be expanded to include a dedicated web app for increased awareness and information dissemination. Further developments could extend the model’s capabilities to detect other activities like fighting, fire detection, burglary, and kidnapping. Customizing the dataset for specific locations can improve the model’s accuracy and effectiveness.
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