On Principles Of Motion Analysis In Real Time

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Many biological vision systems are efficiently equipped for detection and analysis of the essential perceptual primitive of motion. As technology advances, the ambitions of including this ability in computer vision systems appear more and more realistic. However, to become of practical use, real time performance (in some sense) is required, and the current possibilities for this are still limited. Many different approaches to motion analysis, and in particular to its prerequisite motion detection, have been proposed in the literature. Motion information may be derived from image analysis systems at different levels of the general scheme of image processing and interpretation. However, to achieve a result in terms of motion descriptions, most of these methods depend extensively on image preprocessing (and interpretation) or on integration into an image postprocessing (and interpretation) system. A number of methods are reviewed and evaluated with regard to dependency on supplementary processing and with regard to current potential for real time application. Also we discuss their weaknesses due to problems of ambiguity and noise. However, one can take into account that real time operation also means continuous operation and thereby that a temporal context is provided. This allows concentration on changes most of which are predictable, and savings in computing as well as improved robustness to noise and ambiguities can be achieved. In conclusion we find that high level token matching currently is one of the most promising approaches, and an experimental implementation is used to demonstrate a possible approach to motion analysis in real time.