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9 August 2018 Improving methods for detecting people in video recordings using shifting time-windows
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Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 108060Y (2018) https://doi.org/10.1117/12.2502975
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China
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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Adam Blokus and Henryk Krawczyk "Improving methods for detecting people in video recordings using shifting time-windows", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108060Y (9 August 2018); https://doi.org/10.1117/12.2502975
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