Complexity analysis of vision functions for implementation of wireless smart cameras using system taxonomy

Abstract

There are a number of challenges caused by the large amount of data and limited resources when implementing vision systems on wireless smart cameras using embedded platforms. Generally, the common challenges include limited memory, processing capability, the power consumption in the case of battery operated systems, and bandwidth. It is usual for research in this field to focus on the development of a specific solution for a particular problem. In order to implement vision systems on an embedded platform, the designers must firstly investigate the resource requirements for a design and, indeed, failure to do this may result in additional design time and costs so as to meet the specifications. There is a requirement for a tool which has the ability to predict the resource requirements for the development and comparison of vision solutions in wireless smart cameras. To accelerate the development of such tool, we have used a system taxonomy, which shows that the majority of vision systems for wireless smart cameras are common and these focus on object detection, analysis and recognition. In this paper, we have investigated the arithmetic complexity and memory requirements of vision functions by using the system taxonomy and proposed an abstract complexity model. To demonstrate the use of this model, we have analysed a number of implemented systems with this model and showed that complexity model together with system taxonomy can be used for comparison and generalization of vision solutions. The study will assist researchers/designers to predict the resource requirements for different class of vision systems, implemented on wireless smart cameras, in a reduced time and which will involve little effort. This in turn will make the comparison and generalization of solutions simple for wireless smart cameras.