Multi-camera Behaviour Correlation

Abstract

For gaining a more holistic sense of situational awareness, a primary goal for a multi-camera system is to provide a more complete record and survey a trail of object activities in wide-area spaces, both individually and collectively. This allows for a global interpretation of objects’ latent behaviour patterns and intent. In a multicamera system, disjoint cameras with non-overlapping field of views are more prevalent, due to the desire to maximise spatial coverage in a wide-area scene. However, global behaviour analysis across multiple disjoint cameras is hampered by a number of obstacles such as inter-camera visual appearance variation, unknown and arbitrary inter-camera gaps, lack of visual details and crowdedness, and visual context variation. To overcome these obstacles, a key to visual analysis of multi-camera behaviour lies on how well a model can correlate partial observations of object behaviours from different locations in order to carry out ‘joined-up reasoning’. In this chapter, we describe a framework for modelling a joined-up representation of a synchronised global space, within which local activities from different observational viewpoints can be analysed and interpreted holistically. The focus is on developing a suitable mechanism capable of discovering and quantifying unknown correlations in temporal ordering and temporal delays among different camera views.