The introduction of social robots in human-populated environments generates many different challenges to study for a safe and robust collaboration. To support the humans in different collaborative tasks, an autonomous robot should be able to recognize where target objects and persons are and, nonetheless, whether any person is available for interaction. The application of Deep Learning methods in the field of Computer Vision led to relevant improvements in object detection, image segmentation and other meaningful perception tasks. The aim of this thesis is to make use of state-of-the-art convolutional neural networks to build a real-time software system that allows a robot to recognize humans and objects in real-time using a simple RGBD camera, and exploit this information to estimate if the detected humans are available for human-robot interaction based on a scoring system.