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The computer vision systems for postures recognitions may possibly be practical solutions for the health care field as an improvement of healthy aging and as a support to elderly people in their everyday events. In specific, the fall automatic recognition which attract the attention of both computer vision and patterns recognitions communities. Most methods based on wearable sensors, which have the high recognition rates, while some people are unwilling to wear these sensors. Therefore, alternative methods for example vision based techniques have been developed. The proposed method is a vision based technique for one-person posture recognition with the use of convolutional neural network to recognize and classify different classes for the person posture (eg, sit, lie, and stand) in each frame (if available), which firstly detects the person, and for human body shape extraction a background subtraction is applied and each daily activity is corresponding to extracted shape then estimate the bounded-boxes that predictable to surround the person body shape. Moreover, the proposed technique results give the greatest promising solution for indoor monitoring system.