Automated unusual event detection in video surveillance

Fall is an unusual activity and it is a serious problem among the elderly people. In the proposed system, we present an automatic approach for detecting and recognizing falls of elderly people in the home environments using video based technology. The focus is on the protection and assistance to the elderly people. Fall causes a very high risk of the elderly's life that may cause death. The fall incident is automatically extracted from the video data represents itself, unique information that can be used to alert emergency or to make a decision whether the fall is confirmed. The main motivation of this work is to provide such a system which automatically detects the fall and intimate the respective authority. Proposed method uses background subtraction to detect the moving object and mark those objects with a rectangular and elliptical bounding box followed by extracting the features like aspect ratio, fall angle, silhouette height. In the proposed system, an Adaboost classifier to classify the normal and fall event is used. The system is implemented using OpenCV libraries and Python. The accuracy of the proposed system on Le2i dataset is 79.31%.