Recent large and rapid growth in the healthcare sector has contributed to an increase in the elderly population and an increase in life expectancy. One of the important study topics in this field is the automatic fall detection system. Camera-video has been extensively employed recently for applications in surveillance, the home, and healthcare. Therefore a smart fall detection system is focusing on image and video analysis techniques. For that, our scientific work studied an actual vision-based fall detection system. It produces satisfactory outcomes, but there is still room for improvement. The system has a very high recall rate and can detect all falls, but it lacks precision and frequently reports false positives (more than 99 per-cent). In fact, due to the optimum camera quality, several ordinary activities with specific movements, such as wheelchair mobility, or the light changing in an empty room, can be mistaken for falls. To address this problem and increase precision, we propose a post-process approach, hybridizing a CNN model and a Haar Cascade Classifier to determine whether to confirm or reject an alert that has been identified as a fall. The system’s effectiveness will increase while the false positives are decreased.