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Abstract
We propose a novel method for improving algorithms which detect the presence of people in video sequences. Our focus is on algorithms for applications which require reporting and analyzing all scenes with detected people in long recordings. Therefore one of the target qualities of the classification result is its stability, understood as a low number of invalid scene boundaries. Many existing methods process images in the recording separately. The proposed method bases on the observation that real-life videos depict underlying continuous processes. The method is named FSA (Frame Sequence Analyzed). It is applicable for any underlying binary classification algorithm and it improves it by adding an additional result postprocessing step. The performed experiments are based on improving an established face detection algorithm, evaluated on a public dataset. The effectiveness of the FSA method is verified, acquiring very good results – improving the underlying algorithm in terms of all considered error measures. In the end, possible future improvements are discussed.
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Authors (2)
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Adam Blokus Dr inż.
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Details
- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Title of issue:
- Tenth International Conference on Digital Image Processing (ICDIP 2018) strony 121 - 125
- Language:
- English
- Publication year:
- 2018
- Bibliographic description:
- Blokus A., Krawczyk H.: Improving methods for detecting people in video recordings using shifting time-windows// Tenth International Conference on Digital Image Processing (ICDIP 2018)/ ed. Jiang Xudong : , 2018, s.121-125
- DOI:
- Digital Object Identifier (open in new tab) 10.1117/12.2502975
- Verified by:
- Gdańsk University of Technology
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