In this paper, we introduce a novel computer vision-based perception system, dedicated to the autonomous navigation of visually impaired people. A first feature concerns the real-time detection and recognition of obstacles and moving objects present in potentially cluttered urban scenes. To this purpose, a motion-based, real-time object detection and classification method is proposed. The method requires no a priori information about the obstacle type, size, position or location. In order to enhance the navigation/positioning capabilities offered by traditional GPS-based approaches, which are often unreliably in urban environments, a building/landmark recognition approach is also proposed. Finally, for the specific case of indoor applications, the system has the possibility to learn a set of user-defined objects of interest. Here, multi-object identification and tracking is applied in order to guide the user to localize such objects of interest. The feedback is presented to user by audio warnings/alerts/indications. Bone conduction headphones are employed in order to allow visually impaired to hear the systems warnings without obstructing the sounds from the environment. At the hardware level, the system is totally integrated on an android smartphone which makes it easy to wear, non-invasive and low-cost