Design of Fall Detection System: A Dynamic Pattern Approach with Fuzzy Logic and Motion Estimation
محورهای موضوعی : Image ProcessingKhosro Rezaee 1 , Javad Haddadnia 2
1 - Hakim Sabzevari
2 - Hakim Sabzevari
کلید واژه: Video Processing, Gaussian Mixture Model, HSV Conversion, the Elderly’s Falls, Fuzzy Inference System, Motion Estimation,
چکیده مقاله :
Every year thousands of the elderly suffer serious damages such as articular fractures, broken bones and even death due to their fall. Automatic detection of the abnormal walking in people, especially such accidents as the falls in the elderly, based on image processing techniques and computer vision can help develop an efficient system that its implementation in various contexts enables us to monitor people’s movements. This paper proposes a new algorithm, which drawing on fuzzy rules in classification of movements as well as the implementation of the motion estimation, allows the rapid processing of the input data. At the testing stage, a large number of video frames received from CASIA, CAVAIR databases and the samples of the elderly’s falls in Sabzevar’s Mother Nursing Home containing the falls of the elderly were used. The results show that the mean absolute percent error (MAPE), root-mean-square deviation (RMSD) and standard deviation error (SDE) were at an acceptable level. The main shortcoming of other systems is that the elderly need to wear bulky clothes and in case they forget to do so, they will not be able to declare their situation at the time of the fall. Compared to the similar techniques, the implementation of the proposed system in nursing homes and residential areas allow the real time and intelligent monitoring of the people.
Every year thousands of the elderly suffer serious damages such as articular fractures, broken bones and even death due to their fall. Automatic detection of the abnormal walking in people, especially such accidents as the falls in the elderly, based on image processing techniques and computer vision can help develop an efficient system that its implementation in various contexts enables us to monitor people’s movements. This paper proposes a new algorithm, which drawing on fuzzy rules in classification of movements as well as the implementation of the motion estimation, allows the rapid processing of the input data. At the testing stage, a large number of video frames received from CASIA, CAVAIR databases and the samples of the elderly’s falls in Sabzevar’s Mother Nursing Home containing the falls of the elderly were used. The results show that the mean absolute percent error (MAPE), root-mean-square deviation (RMSD) and standard deviation error (SDE) were at an acceptable level. The main shortcoming of other systems is that the elderly need to wear bulky clothes and in case they forget to do so, they will not be able to declare their situation at the time of the fall. Compared to the similar techniques, the implementation of the proposed system in nursing homes and residential areas allow the real time and intelligent monitoring of the people.
