E-V: Efficient visual surveillance with electronic footprints

Video cameras have been deployed at almost every critical location, and they keep generating huge volumes of video data. The current visual processing technologies are not efficient in handling all these data for surveillance purposes, and a large amount of human power is needed to process them. In this paper, we propose the E-V system, which uses electronic footprints to help sort through this swamp of data. Electronic footprints are wireless signals emitted by mobile devices carried by people. They are ubiquitous and amenable to collection and indexing. We study how to use electronic footprints to help quickly and accurately identify object's appearance model from large volumes of video data. We have formulated the problem and provided efficient algorithms to achieve the identification on large data sets. Real world experiments and large-scale simulations have been done, which confirms the feasibility and efficiency of the proposed algorithms.