Data Fusion in Modern Surveillance

The performances of the systems that fuse multiple data coming from different sources are deemed to benefit from the heterogeneity and the diversity of the information involved. The rationale behind this theory is the capability of one source to compensate the error of another, offering advantages such as increased accuracy and failure resilience. While in the past ambient security systems were focused on the extensive usage of arrays of single-type sensors, modern scalable automatic systems can be extended to combine multiple information coming from mixed-type sources. All this data and information can be exploited and fused to enhance situational awareness in modern surveillance systems. From biometrics to ambient security, from robotics to military applications, the blooming of multi-sensor and heterogeneous-based approaches confirms the increasing interest in the data fusion field. In this chapter we want to highlight the advantages of the fusion of information coming from multiple sources for video surveillance purposes. We are thus presenting a survey of existing methods to outline how the combination of heterogeneous data can lead to better situation awareness in a surveillance scenario. We also discuss a new paradigm that could be taken into consideration for the design of next generation surveillance systems.