The National Aeronautics and Space Administration (NASA), along with members of the aircraft industry, recently developed technologies for a new supersonic aircraft. One of the technological areas considered for this aircraft is the use of video cameras and image processing equipment to aid the pilot in detecting other aircraft in the sky. The detection techniques should provide high detection probability for obstacles that can vary from sub-pixel to a few pixels in size, while maintaining a low false alarm probability in the presence of noise and severe background clutter. Furthermore, the detection algorithms must be able to report such obstacles in a timely fashion, imposing severe constraints on their execution time. This paper describes approaches to detect airborne obstacles on collision course and crossing trajectories in video images captured from an airborne aircraft. In both cases, the approaches consist of an image processing stage to identify possible obstacles followed by a tracking stage to distinguish between true obstacles and image clutter, based on their behavior. The crossing target detection algorithm was also implemented on a pipelined architecture from DataCube and runs in real time. Both algorithms have been successfully tested on flight tests conducted by NASA.