K-Nearest Neighbours Based Classifiers for Moving Object Trajectories Reconstruction

Afzal, Muhammad Majid, Ouazzane, Karim and Vassilev, Vassil (2018) K-Nearest Neighbours Based Classifiers for Moving Object Trajectories Reconstruction. In: The Third International Conference on Applications and Systems of Visual Paradigms VISUAL 2018, July 27-28, 2018, Venice, Italy.

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Abstract / Description

This article presents an exemplary prototype implementation of an Application Programming Interface (API) for incremental reconstruction of the trajectories of moving objects captured by Closed-Circuit Television (CCTV) and High-Definition Television (HDTV) cameras based on KNearest Neighbor (KNN) classifiers. This paper proposes a model-driven approach for trajectory reconstruction based on machine learning algorithms which is more efficient than other approaches for dynamic tracking, such as RGB-D (Red, Green and Red Color model with Depth) images or scale or rotation approaches. The existing approaches typically need a low-level information from the input video stream but the environment factors (indoor light, outdoor light) affect the results. The use of a predefined model allows to avoid this since the data is naturally filtered. Experiments on different input video streams demonstrate that the proposed approach is efficient for solving the tracking of moving objects in input streams in real time because it needs less granular information from the input stream. The research reported here is part of a research program of the Cyber Security Research Centre of London Metropolitan University for real-time video analytics with applicability to surveillance in security, disaster recovery and safety management, and customer insight.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Video surveillance; Real-time video analytics; Model-driven motion description; Moving objects tracking; Trajectory reconstruction; Incremental algorithms; Machine learning
Subjects:	000 Computer science, information & general works
Department:	School of Computing and Digital Media
Depositing User:	Bal Virdee
Date Deposited:	09 Jul 2018 08:55
Last Modified:	09 Jul 2018 08:55
URI:	https://repository.londonmet.ac.uk/id/eprint/2595

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