Abstract
Real-time processing the information coming form video, infra-red or electro-optical sources is a challenging task due the uncertainties such as noise and clutter, but also due to the large dimensionalities of the problem and the demand for fast and efficient algorithms. This paper details an approach for automatic detection, single and multiple objects identification and tracking in video streams with applications to surveillance, security and autonomous systems. It is based on a method that provides recursive density estimation (RDE) using a Cauchy type of kernel. The main advantage of the RDE approach as compared to other traditional methods (e.g. KDE) is the low computational and memory storage cost since it works on a frame-by-frame basis; the lack of thresholds, and applicability to multiple objects identification and tracking. A robust to noise and clutter technique based on spatial density is also proposed to autonomously identify the targets location in the frame.
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Sadeghi-Tehran, P., Angelov, P., Ramezani, R. (2010). A Fast Recursive Approach to Autonomous Detection, Identification and Tracking of Multiple Objects in Video Streams under Uncertainties. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications. IPMU 2010. Communications in Computer and Information Science, vol 81. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14058-7_4
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DOI: https://doi.org/10.1007/978-3-642-14058-7_4
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