Multiple-camera people localization in an indoor environment

With the rapid proliferation of video cameras in public places, the ability to identify and track people and other objects creates tremendous opportunities for business and security applications. This paper presents the Multiple Camera Indoor Surveillance project which is devoted to using multiple cameras, agent-based technology and knowledge-based techniques to identify and track people and summarize their activities. We also describe a people localization system, which identifies and localizes people in an indoor environment. The system uses low-level color features – a color histogram and average vertical color – for building people models and the Bayesian decision-making approach for people localization. The results of a pilot experiment that used 32 h of data (4 days × 8 h) showed the average recall and precision values of 68 and 59% respectively. Augmenting the system with domain knowledge, such as location of working places in cubicles, doors and passages, increased the average recall to 87% and precision to 73%.