Multi-sensor data fusion is an emerging research field whose aim is to combine information from multiple and diverse sources (e.g. different sensors – thermal and visible spectrum cameras, laser, range sensors, microphones, RFID etc.) to achieve inferences that cannot be obtained from a single sensor or source, or whose quality exceeds that of an inference drawn from any single source. To cite a few examples: person identification can be improved through a combination of audio (voice) and video (silhouette) cues, or object tracking in adverse weather conditions can take advantage from the fusion of thermal and visible camera images.
Multi-sensor data fusion is inherently a multi-disciplinary subject that draws from such areas as statistical estimation, signal processing, computer vision and machine learning.
PAVIS is concerned with the development of multi-sensor data fusion techniques mainly for automated surveillance applications. In this context, different tasks such as person detection, tracking and re-identification, behavior analysis, and high level scene understanding are addressed, with the aim to investigate potential improvements with a multi-sensor set up through a combination of theoretical analysis and experimental testing.
Due to the multifarious nature of the sensor devices adopted, there are strong interactions with other research areas of PAVIS, such as Acoustic Signal Processing and Visual Geometry and Modeling.
