Abstract
We estimate the speed of texture change by measuring the spread of texture vectors in their feature space. This method allows us to robustly detect even very slow moving objects. By learning a normal amount of texture change over time, we are also able to detect increased activities in videos. We illustrate the performance of the proposed techniques on videos from PETS repository and the Temple University Police department.
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Latecki, L.J., Miezianko, R., Pokrajac, D. (2005). Activity and Motion Detection Based on Measuring Texture Change. In: Perner, P., Imiya, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2005. Lecture Notes in Computer Science(), vol 3587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11510888_47
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DOI: https://doi.org/10.1007/11510888_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26923-6
Online ISBN: 978-3-540-31891-0
eBook Packages: Computer ScienceComputer Science (R0)Springer Nature Proceedings Computer Science
Keywords
- Gaussian Mixture Model
- Motion Detection
- Background Texture
- Principal Component Analysis Component
- Motion Detection Algorithm
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
