Cognitive video surveillance: an ANN/CBR hybrid approach
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Cognitive vision is an interesting field of research that tries to create vision systems with cognitive abilities. Automated video surveillance is increasingly needed to watch our public transport systems, either as a totally automated system or as an operator assistant. In this thesis a design for a cognitive automated video surveillance system is proposed. A hybrid ANN/CBR behavior analysis system is proposed as a cognitive extension of existing video tracking system, and a prototype is developed. By investigating the current state of cognitive vision and automated video surveillance and by looking at existing hybrid approaches to combine them, it is argued that this approach is an area of research where few designs are implemented and tested. Several frameworks for both ANN/CBR hybrids are proposed in literature, and so called emergent cognitive system frameworks are also presented, but few implementations have been done. As far as our literature review has spanned, no automated video surveillance system is attempted with the use of an ANN/CBR hybrid.