<title>Performance benefits and limitations of a camera network</title>

Abstract

Visual information is of vital significance to both animals and artificial systems. The majority of mammals rely on two images, each with a resolution of 10⁷-10⁸ 'pixels' per image. At the other extreme are insect eyes where the field of view is segmented into 10³-10⁵ images, each comprising effectively one pixel/image. The great majority of artificial imaging systems lie nearer to the mammalian characteristics in this parameter space, although electronic compound eyes have been developed in this laboratory and elsewhere. If the definition of a vision system is expanded to include networks or swarms of sensor elements, then schools of fish, flocks of birds and ant or termite colonies occupy a region where the number of images and the pixels/image may be comparable. A useful system might then have 10⁵ imagers, each with about 10⁴-10⁵ pixels. Artificial analogs to these situations include sensor webs, smart dust and co-ordinated robot clusters. As an extreme example, we might consider the collective vision system represented by the imminent existence of ~10⁹ cellular telephones, each with a one-megapixel camera. Unoccupied regions in this resolution-segmentation parameter space suggest opportunities for innovative artificial sensor network systems. Essential for the full exploitation of these opportunities is the availability of custom CMOS image sensor chips whose characteristics can be tailored to the application. Key attributes of such a chip set might include integrated image processing and control, low cost, and low power. This paper compares selected experimentally determined system specifications for an inward-looking array of 12 cameras with the aid of a camera-network model developed to explore the tradeoff between camera resolution and the number of cameras.