An Improved Feature-Based Method for Fall Detection

Aiming at improving the efficiency and accuracy of fall detection, this paper fuses traditional feature-based methods and Support Vector Machine (SVM). The proposed method provides two major improvements. Firstly, the classic features were adopted and together with machine learning technology form an improved and efficient fall detection method. Secondly, the definition of a threshold which needs massive experiments was now learned by the program itself. Compared with the current popular end-to-end deep learning methods, the improved feature-based method fusing machine learning technology shows great advantages in time efficiency because of the significant reduction of the input parameters. Additionally, with the help of SVM, the thresholds need no manual definition, which saves a lot of time and makes it more precise. Our approach is evaluated on a public dataset, TST fall detection dataset v2. The results show that our approach achieves an accuracy of 93.56%, which is better than other typical methods. Furthermore, the approach can be used in real-time video surveillance because of its time efficiency and robustness. Keywords: computer vision; fall detection; feature-based method; handcrafted feature; Support Vector Machine (SVM)