Abstract
A large number of surveillance applications require fast action, and since many surveillance applications, motive objects contain most critical information. Fast detection algorithm system becomes a necessity. A problem in computer vision is the determination of weights for multiple objective function optimizations. In this paper we propose techniques for automatically determining the weights, and discuss their properties. The Min-Max Principle, which avoids the problems of extremely low or high weights, is introduced. Expressions are derived relating the optimal weights, objective function values, and total cost. Simulation results show, compared to the conventional work, it can achieve around 40% time saving and higher detection accuracy for both outdoor and indoor surveillance videos.
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Su, J., Wei, X., Jin, X., Ikenaga, T. (2011). Multi Objective Optimization Based Fast Motion Detector. In: Lee, KT., Tsai, WH., Liao, HY.M., Chen, T., Hsieh, JW., Tseng, CC. (eds) Advances in Multimedia Modeling. MMM 2011. Lecture Notes in Computer Science, vol 6523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17832-0_46
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DOI: https://doi.org/10.1007/978-3-642-17832-0_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17831-3
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