Falls are the major cause of serious injuries and even death for elderly people. Fall detectors are usually based on wearable devices such as gyroscope, accelerometers, etc. Unfortunately, elderly people often forget to wear them especially those with dementia. In this paper, we present a new vision-based method for automatic fall detection in smart home environment. First, we extract efficiency the person silhouette based on background subtraction method and active contour. Then, motion and shape features are extracted from person body parts and analyzed in order to classify fall from other daily activities using rule-based classification. Evaluation results demonstrate the effectiveness of the proposed method in smart home environment.
In this paper, we propose an efficient unsupervised method for mutli-person tracking based on hierarchical level-set approach. The proposed method uses both edge and region information in order to effectively detect objects. The persons are tracked on each frame of the sequence by minimizing an energy functional that combines color, texture and shape information. These features are enrolled in covariance matrix as region descriptor. The present method is fully automated without the need to manually specify the initial contour of Level-set. It is based on combined person detection and background subtraction methods. The edge-based is employed to maintain a stable evolution, guide the segmentation towards apparent boundaries and inhibit regions fusion. The computational cost of level-set is reduced by using narrow band technique. Many experimental results are performed on challenging video sequences and show the effectiveness of the proposed method.
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