Multi camera image tracking

This paper presents a method for multi-camera image tracking in the context of image surveillance. The approach differs from most methods in that we exploit multiple camera views to resolve object occlusion. Moving objects are detected by using background subtraction. Viewpoint correspondence between the detected objects is then established by using the ground plane homography constraint. The Kalman Filter is then used to facilitate the tracking of objects in 3D. Tracking in 3D offers benefits in terms of allowing multiple views to be combined to generate a network field of view (FOV), i.e. the FOV of all the cameras combined. In addition, tracking the objects location in 3D allows us to use the Linear Kalman Filter, which is less cumbersome to implement than the Extended Kalman Filter (EKF). The current system is capable of tracking moving objects within three camera views