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1 2009

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Optical feature clustering algorithm for object tracking in image sequences

Pełczyński P., Arriola J. A.

Image Processing & Communications

2009

Vol. 14, no 2-3

23-30

The aim of the presented work was the development of software technique for detection and tracking of moving objects in video sequences. It is intended to serve as an automatic video surveillance or traffic control system. Local image features are detected and tracked in the presented system. Two clustering algorithms are utilised for this task succes-fully. Firstly, the QT (Quality Threshold) algorithm has a potential of new object detection. Secondly, modification of a well known K-means algorithm proved its usefulness in tracking moving objects in image sequences. For reduction of the analysed data, corners are detected in consecutive images. Their motion vector and coordinates produce feature vectors for an image classifier. The obtained results show the ability of the proposed technique to detect and track multiple objects on the basis of their local, visual features. No model matching technique was necessary, which simplified overall approach. Comparatively low number of operations, required to perform tracking process, gives the possibility to implement the algorithm in real time on modern graphics processing unit in PC computers.