Considering the aging of individuals with the increasing population, the demand for technologies that enable people to follow their daily lives unnoticed is increasing day by day. In this article, a high-performance solution to the fall and posture detection problem for CCD camera-based fall detection systems is provided. Fall detection was performed with images obtained with CCD cameras placed in different positions. Within the scope of the proposed method, two different pre-trained CNN structures were trained using two different camera images. Data fusion was applied to the high-level features obtained from these structures. Features that were fusion process applied to different classifiers were given as input and ensemble learning process was applied. Considering the performance metrics of the proposed method, it was predicted that promising results were obtained for fall detection.