Demo:https://drive.google.com/file/d/1bSvxvQZoZ8WSPkbtczdZ96ub7xxbuKfl/view?usp=sharing
OVERVIEW:
Virtual-Eye is an innovative and advanced life-saving technology designed specifically for swimming pools. It serves as a lifeguard system that employs cutting-edge artificial intelligence and computer vision algorithms to detect active drowning incidents in real-time. By leveraging its state-of-the-art capabilities, Virtual-Eye acts as an extra set of vigilant eyes, constantly monitoring pool activities and ensuring the safety of swimmers.The primary objective of Virtual-Eye is to swiftly identify potential drowning situations as they occur, allowing for immediate intervention and rescue efforts.
PURPOSE:
The purpose of Virtual-Eye is to enhance the safety and security of swimming pools by providing an advanced lifeguard system capable of detecting active drowning incidents. The system aims to: • Save Lives: The primary purpose of Virtual-Eye is to prevent drowning incidents and save lives. • Provide Additional Vigilance: Virtual-Eye acts as an extra set of eyes, continuously monitoring the pool area and alerting lifeguards or designated personnel to potential drowning situations. • Improve Response Time: With its quick detection capabilities, Virtual-Eye significantly reduces the response time to active drowning incidents. By immediately alerting lifeguards, it allows them to respond rapidly, locate the individual in distress, and provide timely assistance. • Minimize False Alarms: Virtual-Eye is designed to differentiate between normal pool activities and genuine drowning scenarios, minimizing false alarms. • Enhance Safety Protocols: The data analytics and reporting features of Virtual-Eye provide valuable insights into pool usage patterns and safety trends. • Integrate with Existing Infrastructure: Virtual-Eye is designed to seamlessly integrate with existing pool management systems. It can be retrofitted into both indoor and outdoor swimming pools, making it a versatile solution that can be customized to suit the specific requirements and layout of each facility.
PROPOSED SOLUTION:
Virtual-Eye presents an innovative and effective solution to address the existing problem of drowning incidents in swimming pools. It utilizes advanced artificial intelligence and computer vision technology to detect active drowning incidents in real-time and provide immediate alerts. The key features of the proposed solution are as follows: Real-time Drowning Detection: Virtual-Eye employs state-of-the-art AI algorithms to analyze video feeds from strategically placed high-resolution cameras around the pool area. The algorithms are trained to recognize specific visual cues associated with active drowning incidents, such as abnormal body movements, lack of movement, and struggling behaviors. This real-time detection capability enables swift intervention and rescue. Instant Alert System: When Virtual-Eye identifies a potential drowning incident, it triggers an instant alert system to notify on-site lifeguards or designated personnel. Integration and Customization: The proposed solution is designed to seamlessly integrate with existing pool management systems and infrastructure. It can be customized to suit the specific requirements and layout of each facility, allowing for easy installation and adaptation. By implementing Virtual-Eye as a lifeguard system in swimming pools, the proposed solution aims to significantly enhance drowning detection and response capabilities. Its advanced technology, real-time monitoring contributes to a safer swimming environment and a reduced risk of drowning incidents.
THEORITICAL ANALYSIS:
Virtual-Eye, as a life guard system for swimming pools, can utilize the YOLOV3 (You Only Look Once) model to enhance its ability to detect active drowning incidents. Theoretical analysis of Virtual-Eye using the YOLO model: • YOLO Model for Drowning Detection: The YOLO model can be trained to detect specific visual cues associated with active drowning incidents. By utilizing a large dataset of drowning scenarios, the model can learn to recognize important features such as abnormal body movements, lack of movement, and struggling behaviors. The theoretical analysis involves training the YOLO model on a diverse and representative drowning dataset to ensure its effectiveness. • Training Data Availability: The analysis should consider the availability and quality of training data for the YOLO model. An extensive dataset containing labelled images or videos of drowning incidents in swimming pools is necessary to train the model effectively. The analysis should evaluate the accessibility of such data and explore potential strategies for data augmentation to enhance the model's performance. • Model Optimization: It involves optimizing the YOLO model to achieve high accuracy and efficiency. This includes architectural modifications, hyperparameter. The analysis should investigate various optimization approaches to ensure that the YOLO model meets the real-time requirements of drowning detection in swimming pools. • Integration with Virtual-Eye: It consider the integration of the YOLO model into the Virtual-Eye system. This involves developing a pipeline to process the video feeds from the surveillance cameras in real-time, applying the YOLO model for object detection, and specifically identifying potential drowning incidents. The analysis should evaluate the computational requirements, latency, and compatibility of integrating the YOLO model into the Virtual-Eye infrastructure. By conducting a comprehensive theoretical analysis of Virtual-Eye's integration with the YOLO model, it is possible to evaluate and optimize the system's drowning detection capabilities. The analysis should focus on training data availability, model optimization, integration feasibility, real-time processing, false alarm mitigation, and long-term model maintenance to ensure the effectiveness and reliability of Virtual-Eye as a life guard system for swimming pools.
FLOW:
Steps:
- Prepare a dataset for Swimming and Drowning (collection of Swimming and Drowning images to train the model)
- Annotate the dataset by labeling the drowning instances and storing the annotations in text files (e.g., YOLO format).
- Obtain pre-trained YOLO weights as a starting point for training.
- Train the YOLO model on annotated dataset.
- Integrate Flask with the web page for hosting locally.
- Design the web page with an input field for uploading an image or video.
- When an image or video is uploaded, sent it to the model for testing
- Obtain the output predictions and results
- Display the output, such as bounding boxes and labels, on the web page
CONCLUSION:
In this project, we developed a drowning detection system for swimming pools using the YOLOv3 object detection algorithm. We gathered a dataset of swimming pool images or videos, annotated it, and trained the YOLOv3 model with our custom dataset. By integrating the model with Flask and a web page, we created a user-friendly interface for testing the system with real-world images, videos, or live webcam feeds.Through our project, we aimed to enhance pool safety and provide early detection of potential drowning incidents. By leveraging the accuracy and efficiency of YOLOv3, we achieved reliable object detection, specifically focusing on identifying drowning instances in swimming pools.Overall, our drowning detection system shows promise in enhancing pool safety and providing valuable assistance to lifeguards or pool owners. The real-time detection capability, combined with the user-friendly interface, offers a practical solution for preventing drowning incidents