Abstract
The paper focuses on motion-based information extraction from video imagesequences. A novel method is introduced which can reliably detect walking human figures contained in such images. The method works with spatiotemporal input information to detect and classify the patterns typical of human movement. Our algorithm consists of easy-to-optimise operations, which in practical applications is an important factor. The paper presents a new information-extraction and temporal-tracking method based on a simplified version of the symmetry which is characteristic for the legs of a walking person. These spatio-temporal traces are labelled by kernel Fisher discriminant analysis. With this use of temporal tracking and non-linear classification we have achieved pedestrian detection from real-life images with a correct classification rate of 96.5%.
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Havasi, L., Szlávik, Z., Szirányi, T. (2006). PEDESTRIAN DETECTION USING DERIVED THIRD-ORDER SYMMETRY OF LEGS A novel method of motion-based information extraction from video image-sequences. In: Wojciechowski, K., Smolka, B., Palus, H., Kozera, R., Skarbek, W., Noakes, L. (eds) Computer Vision and Graphics. Computational Imaging and Vision, vol 32. Springer, Dordrecht. https://doi.org/10.1007/1-4020-4179-9_106
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DOI: https://doi.org/10.1007/1-4020-4179-9_106
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