With the increasing aging of the global population, the safety of the elderly has become a hot issue of social concern, among which falls are one of the main causes of injury and even death to the elderly. An effective fall detection system can detect falls in a timely manner, initiate emergency response measures quickly, and significantly reduce the serious consequences caused by falls. In addition, the development of fall detection technology also plays an important role in promoting intelligent health care, intelligent monitoring and other fields.
Considering the spatial structure information of human skeletal data, a graph convolutional network-based method is proposed for fall detection. The method first extracts bone information from the video using pose estimation technology, and then analyzes the dynamic relationship between bones and joints through the GCN model. The innovation of this method lies in effectively capturing the spatial characteristics of human motion, thereby improving the accuracy of detection. The test results on standard CAD-60 and NTU RGB + D datasets show that the method has significant performance improvement compared with traditional video frame analysis methods.
The work done in this blog is to build a fall detection system based on the YOLOv8 algorithm, show the interface effect of the system, explain the principle of its algorithm in detail, provide code implementation, and share the implementation process of the system. I hope the sharing of this blog can give readers some inspiration and promote more related research. The main contributions of this paper are as follows:
Adopting the most advanced YOLOv8 algorithm for fall detection: This paper not only introduces the construction process of a fall detection system based on YOLOv8 algorithm, but also compares the performance differences of earlier versions such as YOLOv73, YOLOv64, and YOLOv55 in detail. Through in-depth analysis and comparison of these algorithms, this paper demonstrates the significant advantages of YOLOv8 in terms of detection speed, accuracy, and efficiency, providing new research ideas and tools for researchers and technicians in the field of fall detection.
Implementing a user-friendly fall detection system with PySide6: By using the PySide6 library, this paper successfully developed a fall detection system with an excellent user interface. The system is not only easy to operate, but also highly interactive and visual, which greatly promotes the application of YOLOv8 algorithm in the field of fall detection, and also promotes the practical implementation of the technology.
Integrate login management functions to improve system security: The fall detection system designed in this paper includes login management functions to ensure the security and privacy of system use. This innovative move not only provides users with a personalized experience, but also provides a strong foundation for future system expansion and feature addition.
In-depth study and performance evaluation of the YOLOv8 model: This paper not only applies the YOLOv8 algorithm, but also conducts a comprehensive study and evaluation of its performance, including in-depth analysis of key performance indicators such as precision and recall rate. By evaluating the performance of the model in different environments and conditions, this paper provides a solid theoretical and practical foundation for the further optimization and improvement of the YOLOv8 algorithm.
Provide a complete dataset and code effects package: In order to promote the research and application of YOLOv8 and its predecessors in fall detection systems, this paper provides a complete dataset and code effects package. These resources not only enable readers to easily reproduce experimental results, but also lay the foundation for further research and development based on this work.
The system provides functions that support picture, video, real-time camera and batch file input. Users can select the picture or video to be detected by falling by clicking the corresponding button, or start the camera for real-time detection. During the detection, the system will display the detection results in real time and store the detection records in the database.
The system also provides the function of one-click replacement of YOLOv8 models. Users can select a different YOLOv8 model for detection by clicking the “Change Model” button on the interface. At the same time, the dataset attached to the system can also be used to retrain the model to meet the user’s detection needs in different scenarios.
Compare and evaluate the performance of different versions of YOLO models — including YOLOv5nu, YOLOv6n, YOLOv7-tiny, and YOLOv8n — on the fall detection task
Looking at the mAP values, we can see that YOLOv8n leads with a score of 0.897, indicating that in the object detection task, YOLOv8n has the highest average accuracy in recognizing fall events. This is followed by YOLOv5nu and YOLOv6n, which have mAP values of 0.884 and 0.880, respectively, showing fairly close performance. YOLOv7-tiny has a mAP value of 0.841, which is slightly lower in comparison
Main code:
frame_process function defines how each frame of image is processed. In this function, we first adjust the image size to fit the input requirements of the YOLOv8 model. Then, the image data is prepared by preprocess method and object detection is performed using predict method. After each inference, we record and print the time taken, which helps us understand how the model performs in practical applications. If a fall event is detected, we use drawRectBox function to draw bounding boxes on the image and assign colors to different categories through get_cls_color function, making the result intuitive and easy to understand.
If you have any questions or need the full code or data or need customized development requirements, you can contact me Email Mail me : slowlon@foxmail.com