Early detection plays a crucial role to prevent forest fires from spreading. Wireless vision sensor
networks deployed throughout high-risk areas can perform fine-grained surveillance and thereby
very early detection and precise location of forest fires. One of the fundamental requirements that
need to be met at the network nodes is reliable low-power on-site image processing. It greatly
simplifies the communication infrastructure of the network as only alarm signals instead of
complete images are transmitted, anticipating thus a very competitive cost. As a first
approximation to fulfill such a requirement, this paper reports the results achieved from field tests
carried out in collaboration with the Andalusian Fire-Fighting Service (INFOCA). Two controlled
burns of forest debris were realized (www.youtube.com/user/vmoteProject). Smoke was
successfully detected on-site by the EyeRISTM v1.2, a general-purpose autonomous vision system,
built by AnaFocus Ltd., in which a vision algorithm was programmed. No false alarm was
triggered despite the significant motion other than smoke present in the scene. Finally, as a further
step, we describe the preliminary laboratory results obtained from a prototype vision chip which
implements, at very low energy cost, some image processing primitives oriented to environmental
monitoring.Ministerio de Ciencia e Innovación 2006-TIC-2352, TEC2009-1181
