A bio-inspired logical process for saliency detections in cognitive crowd monitoring

It is well known from physiological studies that the level of human attention for adult individuals rapidly decreases after five to twenty minutes (Green and Sandia, 1999). Attention retention for a surveillance operator represents a crucial aspect in Video Surveillance applications and could have a significant impact in identifying relevance, especially in crowded situations. In this field, advanced mechanisms for selection and extraction of saliency information can improve the performances of autonomous video surveillance systems and increase the effectiveness of human operator support. In particular, crowd monitoring represents a central aspect in many practical applications for managing and preventing emergencies due to panic and overcrowding. In this paper, an adaptive inductive reasoning mechanism for saliency extraction and information reconstruction for a distributed camera sensors network is presented. The proposed system, by means of Self Organizing Maps (SOMs), can learn the correlation of the observed data and then recover the whole information from a subset of available sensors. Experimental results show how the proposed system can reconstruct the information about the non-observed parts starting from relevant data acquired from observed areas of the environment.