Multimodal assessment of shopping behavior

Abstract

An increasing number of public places (e.g. cities, schools, transit districts, and public buildings) are deploying CCTV surveillance systems to monitor and protect the people in those areas. Since events like the terrorist attacks in Madrid and London, there has been a further increasing demand for video sensor network systems to guarantee the safety of people in public areas. But also events like football games, music concerts and large venues like shopping malls where many people gather, have a need for video surveillance systems to guarantee safety and monitor the behavior of people in these places. Currently, the existing video surveillance systems in public places are used by human operators for moni- toring the situation, analysing the data, and detecting abnormal or unwanted human behaviour such as theft or aggression or for later retrieval in case an unwanted event was reported. Human monitoring has benefits such as intelligent reasoning about the situation, but also limitations such as fatigue or loss of concentration, espe- cially when nothing happens for a long period of time, or difficulty to cope with the overwhelming number of cameras and to watch them continuously. Therefore, a supporting alternative is represented by the development of automatic systems designated at monitoring the video streams and signalising the human operators only in the case of unusual or unwanted events.