No abstract

Fall detection became a major concern especially for elderly who lives alone at home. Unexpected situations might happen that influence their health, security and well-being. The development of an intelligent surveillance system is required to alleviate the negative effects of unforeseen circumstances and assisting the elderly in independent living. Currently, convolutional neural network has been successfully used for solving various computer vision tasks, such as object detection and recognition. In this paper, we propose a new vision system for elderly fall detection based on new two stream convolutional neural networks. First, human silhouette is extracted based on background subtraction and person recognition. Second, history of binary motion image HBMI is fed into the first stream characterizing the human shape variations. The second stream is based on amplitude and orientation of optical flow defining the velocity and the direction of the human motion. The system classifies fall events using score fusion schema. Transfer learning is performed to deal with the small amount of fall datasets. Our final network outperforms state-of-the-art results on standard fall datasets.

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in this paper, we propose an efficient unsupervised method for moving object detection and tracking. To achieve this goal, we use basically a region-based level-set approach and some conventional methods. Modeling of the background is the first step that initializes the following steps such as objects segmentation and tracking. Our proposed method produces good results and decreases processing time. We present here the main steps of our method and preliminary results which are very encouraging for many applications such as video surveillance and traffic monitoring.