Hybrid Attributes Technique Filter for the Tracking of Crowd Behavior

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Author(s): Hocine Chebi (Faculty of Electrical Engineering, Djillali Liabes University, Sidi Bel Abbes, Algeria)
Copyright: 2021
Pages: 10
Source title: Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science
Source Author(s)/Editor(s): Mrutyunjaya Panda (Utkal University, India)and Harekrishna Misra (Institute of Rural Management, Anand, India)
DOI: 10.4018/978-1-7998-6659-6.ch003

Keywords: Data Mining and Databases / Engineering Science Reference / Knowledge Management and Library Science / Machine Learning

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Abstract

In this chapter, the authors propose two algorithms based on the device of attributes for tracking of the abnormal behavior of crowd in the visual systems of surveillance. Previous works were realized in the case of detection of behavior, which uses the analysis and the classification of behavior of crowds; this work explores the continuity in the same domain, but in the case of the automatic tracking based on the techniques of filtering one using the KALMAN filter and particles filter. The proposed algorithms he the technique of filter with particle is independent from the detection and from the segmentation human, so is strong with regard to (compared with) the filter of Kalman. In conclusion, the chapter applies the method for tracking of the abnormal behavior to several videos and shows the promising results.

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Hybrid Attributes Technique Filter for the Tracking of Crowd Behavior

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