The advances in VLSI technology and embedded computing have enabled the introduction of smart cameras, which are stand-alone units that combine sensing, processing and communication on a single embedded platform. With embedded smart cameras, it has now become viable to install many spatially-distributed cameras interconnected by wireless links. Yet, wireless and battery-powered, embedded smart camera networks introduce many additional challenges since they have very limited resources, such as energy, processing power, memory and bandwidth. Computer vision algorithms running on these camera boards should be lightweight and efficient. Considering the memory requirements of an algorithm and its portability to an embedded processor should be an integral part of the algorithm design in addition to the accuracy requirements.