Bio-Inspired Active Vision Paradigms in Surveillance Applications

Abstract

Visual perception was described by Marr (1982) as the processing of visual stimuli through three hierarchical levels of computation. In the first level or low-level vision it is performed the extraction of fundamental components of the observed scene such as edges, corners, flow vectors and binocular disparity. In the second level or medium-level vision it is performed the recognition of objects (e.g. model matching and tracking). Finally, in the third level or high-level vision it is performed the interpretation of the scene. A complementary view is presented in (Ratha & Jain, 1999; Weems, 1991); by contrast, the processing of visual stimuli is analysed under the perspective developed by Marr (1982) but emphasising how much data is being processed and what is the complexity of the operators used at each level. Hence, the low-level vision is characterised by large amount of data, small neighbourhood data access, and simple operators; the medium-level vision is characterised by small neighbourhood data access, reduced amount of data, and complex operators; and the high-level vision is defined by non-local data access, small amount of data, and complex relational algorithms. Bearing in mind the different processing levels and their specific characteristics, it is plausible to describe a computer vision system as amodular framework in which the low-level vision processes can be implemented by using parallel processing engines like GPUs and FPGAs to exploit the data locality and the simple algorithmic operations of the models; and the medium and high-level vision processes can be implemented by using CPUs in order to take full advantage of the straightforward fashion of programming these kind of devices.