Multi-view calibration from planar motion trajectories

Abstract We present a technique for the registration of a network of surveillance cameras through the automatic alignment of observed planar motion trajectories. The algorithm addresses the problem of recovering the relative pose of several stationary, networked cameras whose intrinsic parameters are known. Each camera tracks several objects to produce a set of image trajectories. Using temporal and geometric constraints derived from the trajectory and a network synchronization signal, overlapping viewing frustums are determined and corresponding cameras are calibrated. Full calibration is a two stage process. Initially, the relative orientation of each camera to the local ground plane, is computed in order to recover the projective mapping of image points to world trajectories embedded on a nominal plane of correct orientation. Given the relative camera-to-plane orientation, projectively unwarped trajectory curves can then be robustly matched by solving for the similarity transform that brings them into absolute alignment. Registration aligns n-cameras with respect to each other in a single camera frame (that of the reference camera).The approach recovers both the epipolar geometry between all cameras and the camera-to-ground rotation for each camera independently. After calibration, points that are known to lie on a world ground plane can be directly back projected into each of the camera frames. These tracked points are known to be in spatial and temporal correspondence, supporting multi-view surveillance and motion understanding tasks. The algorithm is demonstrated for two, three, and five camera scenarios by tracking pedestrians as they move through a surveillance area and matching the resulting trajectories.