Detection of aircraft in video sequences using a predictive optical flow algorithm

This paper presents a computer vision algorithm that segre- gates spurious optical flow artifacts to detect a moving object. The algo- rithm consists of six steps. First, the pixels in each image are shifted to compensate for camera rotation. Second, the images are smoothed with a spatiotemporal Gaussian filter. Third, the optical flow is computed with a gradient-based technique. Fourth, optical flow vectors with small mag- nitudes are discarded. Fifth, vectors with similar locations, magnitudes, and directions are clustered together using a spatial consistency test. Sixth, similar optical flow vectors are extended temporally to make pre- dictions about future optical flow locations, magnitudes, and directions in subsequent frames. The actual optical flow vectors that are consistent with those predictions are associated with a moving object. This algo- rithm was tested on images obtained with a video camera mounted be- low the nose of a Boeing 737. The camera recorded two sequences containing a second flying aircraft. The algorithm detected the aircraft in 82% of the frames from the first sequence and 78% of the frames from the second sequence. In each sequence, the false-alarm rate was zero. These results illustrate the effectiveness of using a comprehensive pre- dictive technique when detecting moving objects. © 1999 Society of Photo- Optical Instrumentation Engineers. (S0091-3286(99)01603-7)

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