Abstract.A five-camera vision system was developed for UAV visual attitude calculation and collision warning. The vision system acquires images by using five miniature cameras, stores, and evaluates the visual data real-time with a multi-core processor system implemented in FPGA. The system was designed to be able to operate on a medium sized UAV platform, which raised numerous strict physical constraints.

A real-time vision system with multiple cameras was developed for UAV collision warning and visual navigation for fixed-wing small-or medium-sized aircrafts. The embedded vision system simultaneously acquires images using five cameras, stores, and evaluates the visual data with an FPGA-based multi-core processor system. The system was designed to fulfill the strict size, power, and weight requirements arising from UAV on-board restrictions. The hardware parameters of the system and the performance of the algorithm are compared to the state of the art.

A vision based vehicle speed measurement method is presented in this paper. The proposed intraframe method calculates speed estimates based on a single frame of a single camera. With a special double exposure, a superimposed image can be obtained, where motion blur appears significantly only in the bright regions of the otherwise sharp image. This motion blur contains information of the movement of bright objects during the exposure. Most papers in the field of motion blur are aiming at the removal of this image degradation effect. In this work, we utilize it for a novel speed measurement approach. An applicable sensor structure and exposure-control system are also shown, as well as the applied image processing methods and experimental results.