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Erschienen in:
2017 | OriginalPaper | Buchkapitel
Abnormal Gait Detection in Surveillance Videos with FFT-Based Analysis on Walking Rhythm
verfasst von : Anqin Zhang, Su Yang, Xinfeng Zhang, Jiulong Zhang, Weishan Zhang
Erschienen in: Image and Graphics
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Abstract
For abnormal gait detection in surveillance videos, the existing methods suffer from that they are unable to recognize novel types of anomalies if the corresponding prototypes have not been included in the training data for supervised machine learning but it is impractical to foresee all types of anomalies. This research aims to solve the problem in an unsupervised manner, which does not rely on any prior knowledge regarding abnormal prototypes and avoids time-consuming machine learning over large-scale high-dimensional features. The intuition is that normal gait is nearly periodic signal and anomalies may disturb such periodicity. Hence, the time-varying ratio of width to height of a walking person is transformed to frequency domain using Fast Fourier Transform (FFT), and the standard deviation over spectrum is used as an indicator of anomalies, subject to any sudden change to break the normally periodical walking rhythm. The experimental results demonstrate its precision.