Applying CNN-based object detection models to the task of weapon detection in video-surveillance is still producing a high number of false negatives. In this context, most existing works focus on one type of weapons, mainly firearms, and improve the detection using different pre- and post-processing strategies. One interesting approach that has not been explored in depth yet is the exploitation of the human pose information for improving weapon detection. This paper proposes a top-down methodology that first determines the hand regions guided by the human pose estimation then analyzes those regions using a weapon detection model. For an optimal localization of each hand region, we defined a new factor, called Adaptive pose factor, that takes into account the distance of the body from the camera. Our experiments show that this top-down Weapon Detection over Pose Estimation (WeDePE) methodology is more robust than the alternative bottom-up approach and state-of-the art detection models in both indoor and outdoor video-surveillance scenarios.