Complexity Analysis of Vision Functions for Comparison of Wireless Smart Cameras

There are a number of challenges caused by the large amount of data and limited resources such as memory, processing capability, energy consumption, and bandwidth, when implementing vision systems on wireless smart cameras using embedded platforms. It is usual for research in this field to focus on the development of a specific solution for a particular problem. There is a requirement for a tool which facilitates the complexity estimation and comparison of wireless smart camera systems in order to develop efficient generic solutions. To develop such a tool, we have presented, in this paper, a complexity model by using a system taxonomy. In this model, we have investigated the arithmetic complexity and memory requirements of vision functions with the help of system taxonomy. To demonstrate the use of the proposed model, a number of actual systems are analyzed in a case study. The complexity model, together with system taxonomy, is used for the complexity estimation of vision functions and for a comparison of vision systems. After comparison, the systems are evaluated for implementation on a single generic architecture. The proposed approach will assist researchers in benchmarking and will assist in proposing efficient generic solutions for the same class of problems with reduced design and development costs. 1. Introduction Vision systems implemented on wireless smart cameras have recently been the focus of research for many applications including surveillance [1], recognition [2], traffic monitoring, personal care [3], and industrial monitoring [4]. Often, a number of wireless smart cameras are spread over an area to form a network called a Wireless Vision Sensor Network (WVSN). In the WVSN context, the individual wireless smart camera is referred to as a Wireless Vision Sensor Node (VSN). Each VSN consists of an image sensor, an embedded processing platform, memory, battery or an alternative energy source, and a wireless link. Designing a VSN on an embedded platform is a challenging task because resources are limited compared to those for general purpose platforms. General purpose platforms offer greater design and implementation flexibility; however, these systems are often considered as being unsuitable for real time applications. Therefore, our focus is on vision systems implemented on embedded platforms. When designing a VSN for a particular application, the designers must firstly investigate the complexity of the design, and a failure to do this may result in both increased design costs and a longer developmental cycle. The vision functions can be