Automatic Detection of Human Fall in Video
In this paper, we present an approach for human fall detection, which has important applications in the field of safety and security. The proposed approach consists of two parts: object detection and the use of a fall model. We use an adaptive background
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Abstract. In this paper, we present an approach for human fall detection, which has important applications in the field of safety and security. The proposed approach consists of two parts: object detection and the use of a fall model. We use an adaptive background subtraction method to detect a moving object and mark it with its minimum-bounding box. The fall model uses a set of extracted features to analyze, detect and confirm a fall. We implement a two-state finite state machine (FSM) to continuously monitor people and their activities. Experimental results show that our method can detect most of the possible types of single human falls quite accurately.
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Introduction
Human fall is one of the major health problems for elderly people. Falls are dangerous and often cause serious injuries that may even lead to death. Fall related injuries have been among the five most common causes of death amongst the elderly population. Falls represent 38% of all home accidents and cause 70% of death in the 75+ age group. It is shown in [1] that the number of reported human falls per year was around 60,000 with an associated cost of at least £400 million in the UK. Early detection of a fall is an important step in avoiding any serious injuries. An automatic fall detection system can help to address this problem by reducing the time between the fall and arrival of required assistance. Here, we present an approach for human fall detection using a single camera video sequence. Our approach consists of two steps: object detection and the use of a fall model. We apply an adaptive background subtraction method to detect a moving object and mark it with its minimum-bounding box. The fall model consists of two parts: fall detection and fall confirmation. It uses a set of extracted features to analyze, detect and confirm a fall. In the fall model, the first two features (aspect ratio, horizontal and vertical gradient values of an object) are responsible for fall detection and the third feature (fall angle) is used for fall confirmation. We also implement a two-state finite state machine to continuously monitor people and their activities. The organization of the paper is as follows. Section 2 explains the related work on fall detection. Section 3 describes the object detection method. Section 4 elaborates the fall model. In Section 5, we present experimental results followed by conclusion in section 6. A. Ghosh, R.K. De, and S.K. Pal (Eds.): PReMI 2007, LNCS 4815, pp. 616–623, 2007. c Springer-Verlag Berlin Heidelberg 2007
Automatic Detection of Human Fall in Video
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Related Work
Primarily, there are three methods of fall detection, classified in the following categories: 1. Acoustics based Fall Detection 2. Wearable Sensor based Fall Detection 3. Video based Fall Detection In video based fall detection, human activity is captured in a video that is further analyzed using image processing techniques. Since video cameras have been widely used for surveillance as well as home and health care applications, we use this approach for our f
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